Top 10 Arxiv Papers Today


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#1. SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color
Youngjoo Jo, Jongyoul Park
We present a novel image editing system that generates images as the user provides free-form mask, sketch and color as an input. Our system consist of a end-to-end trainable convolutional network. Contrary to the existing methods, our system wholly utilizes free-form user input with color and shape. This allows the system to respond to the user's sketch and color input, using it as a guideline to generate an image. In our particular work, we trained network with additional style loss which made it possible to generate realistic results, despite large portions of the image being removed. Our proposed network architecture SC-FEGAN is well suited to generate high quality synthetic image using intuitive user inputs.
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samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJwnKM7Zg https://t.co/RXXKfwfXvN
Reza_Zadeh: Editing photos of faces using basic sketches, and letting a GAN do the rest. Lets you add/change: earrings, glasses, hair style, dimples, & more. Paper: https://t.co/rnVyh6yVp9 Code: https://t.co/yuWgQ8EN3E https://t.co/zleKI1TNDH
amywebb: While we delay international guardrails for AI, GANs are going to make it very easy for everyday people to manipulate images using photo editing apps on their phones. #TheBigNine New ppr from ERTI (pdf): https://t.co/A8zygGV8p9 In action: https://t.co/aWrdbOscjV https://t.co/hePMipgJeo
arxiv_org: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. https://t.co/SNLJWgrW6a https://t.co/JpgyNVwVN1
mart1oeil: Wouh ! Un outil d'édition d'images de visages assez puissant : SC-FEGAN (Face Editing Generative Adversarial Network with User's Sketch and Color) https://t.co/udngvAmi70 L'article dans arXiv : https://t.co/wphDFII2Fi via @nahuelcleo https://t.co/gN2QYVnIKh
BrundageBot: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. Youngjoo Jo and Jongyoul Park https://t.co/l6EIMP7vUt
xsteenbrugge: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color Paper: https://t.co/xf9FZn7i3a Code: https://t.co/Pa6qCvDZEe https://t.co/ecpfmu3Mpx
KouroshMeshgi: and now we have this: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/buWsccYlSn https://t.co/Eweywo9G9a …
arxivml: "SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color", Youngjoo Jo, Jongyoul Park https://t.co/YDZmkSAznI
ahammami0: SC-FEGAN : Face Editing Generative Adversarial Network with User's Sketch and Color Youngjoo Jo, Jongyoul Park arXiv: https://t.co/MWowPB5dgL Code: https://t.co/lOhSwugrTy #AI #ArtificialIntelligence #machinelearning #deeplearning #ComputerVision https://t.co/IcWkDbMron
KostyOr: нас ждёт интересное https://t.co/YguupGKLoR
DanilBaibak: SC-FEGAN: Face Editing Generative Adversarial Network with User’s Sketch and Color - architecture that lets you add/change: earrings, glasses, hair style, dimples and more #python #DeepLearning #TensorFlow ArXiV: https://t.co/5ltlpK8Rep Code: https://t.co/tKOg2Rh3it https://t.co/77QyjvJvb0
larsr: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color https://t.co/yDROmOKnZV https://t.co/nBBYWU7bRU
arxiv_cscv: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color https://t.co/xbpxhp1LbC
arxiv_cscv: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color https://t.co/xbpxhoK9N2
arxiv_cscv: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color https://t.co/xbpxhp1LbC
alexanderNWild: Another day, another GAN-made wonder. A stick-figure quality edit can be made fully photorealistic. Of course this means that now photo images are unreliable evidence, due to ease of falsification by any lay person. #GAN #disinformation #ml https://t.co/YnWLw1Noe9 https://t.co/D1wJVxQeP4
bbriniotis: RT @arxiv_org: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. https://t.co/SNLJWgrW6a https://t.co/Jpg…
daveaitel: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
AmberBaldet: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
BigVanCiencia: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
montrealdotai: RT @arxiv_org: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. https://t.co/SNLJWgrW6a https://t.co/Jpg…
StartupYou: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
samim: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
mccandelish: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
ceobillionaire: RT @arxiv_org: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. https://t.co/SNLJWgrW6a https://t.co/Jpg…
mark_riedl: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
punkstrategy: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
DotCSV: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
ajlopez: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
BartWronsk: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
edersantana: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
kevinschawinski: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
Madedigital: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
EMostaque: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
KeefJudge: RT @arxiv_org: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. https://t.co/SNLJWgrW6a https://t.co/Jpg…
HCI_Research: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
ramonsang: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
ballforest: RT @arxiv_org: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. https://t.co/SNLJWgrW6a https://t.co/Jpg…
IntuitMachine: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
minxdragon: RT @xsteenbrugge: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color Paper: https://t.co/xf9FZn7i3a Code:…
sarahbadr: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
syoyo: RT @arxiv_org: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. https://t.co/SNLJWgrW6a https://t.co/Jpg…
ayirpelle: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
jaguring1: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
mosko_mule: RT @arxiv_org: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. https://t.co/SNLJWgrW6a https://t.co/Jpg…
yshhrknmr: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
designmeaning: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
dh7net: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
morenovski: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
Hector_Pulido_: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
joelmartinez: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
Yokohara_h: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
yuunagi: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
yuunagi: RT @arxiv_org: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. https://t.co/SNLJWgrW6a https://t.co/Jpg…
highqualitysh1t: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
multihyphenate: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
sternutarantoga: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
sei_shinagawa: RT @arxiv_org: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. https://t.co/SNLJWgrW6a https://t.co/Jpg…
proc_gen: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
keunwoochoi: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
shunk031: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
KageKirin: RT @arxiv_org: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. https://t.co/SNLJWgrW6a https://t.co/Jpg…
udmrzn: RT @arxiv_org: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. https://t.co/SNLJWgrW6a https://t.co/Jpg…
apstndb: RT @arxiv_org: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. https://t.co/SNLJWgrW6a https://t.co/Jpg…
bratrat: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
Tbeltramelli: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
knto_h: RT @arxiv_org: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. https://t.co/SNLJWgrW6a https://t.co/Jpg…
jesusprubio: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
permutans: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
fursund: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
BrianKrent: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
RexDouglass: RT @arxiv_org: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. https://t.co/SNLJWgrW6a https://t.co/Jpg…
rrrelll: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
ialuronico: RT @arxiv_org: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. https://t.co/SNLJWgrW6a https://t.co/Jpg…
Zerofever: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
liotier: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
satie_no2: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
wakame1367: RT @arxiv_org: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. https://t.co/SNLJWgrW6a https://t.co/Jpg…
briancleland: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
muktabh: RT @arxiv_org: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. https://t.co/SNLJWgrW6a https://t.co/Jpg…
OSCityNL: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
soichimatsuda: RT @arxiv_org: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. https://t.co/SNLJWgrW6a https://t.co/Jpg…
chauchau0: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
gabrielstuff: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
hobbitbit: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
agulosso: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
arthurostapenko: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
viperale: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
jiimiettinen: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
tawatawara: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
matonis: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
warbird: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
ecrws: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
l4rz: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
JackreeceEjini: RT @arxiv_org: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. https://t.co/SNLJWgrW6a https://t.co/Jpg…
analyticsaurabh: RT @arxiv_org: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. https://t.co/SNLJWgrW6a https://t.co/Jpg…
HenkPoley: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
camsooper: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
YannLePaih: RT @arxiv_org: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. https://t.co/SNLJWgrW6a https://t.co/Jpg…
30eesti: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
DUXROLL: RT @arxiv_org: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. https://t.co/SNLJWgrW6a https://t.co/Jpg…
beckerfuffle: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
Involution88: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
ahdamia: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
KouroshMeshgi: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
spartanhaden: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
RealtimeAI: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
RomainClaret: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
kuzu_kuzu_man: RT @arxiv_org: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. https://t.co/SNLJWgrW6a https://t.co/Jpg…
PSFog: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
tiffkwin: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
zuiko21: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
jadhavamitb: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
cortexelation: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
mutsucity: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
madelain777: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
intelligenz_b: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
robert_tilt: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
devjoolz: RT @arxiv_org: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. https://t.co/SNLJWgrW6a https://t.co/Jpg…
ryosanworld: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
Iain_Keaney: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
lucas_tejero: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
rhemalinder: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
_Muratter_: RT @arxiv_org: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. https://t.co/SNLJWgrW6a https://t.co/Jpg…
vodkamomo: RT @arxiv_org: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. https://t.co/SNLJWgrW6a https://t.co/Jpg…
XitoVicious: RT @arxiv_org: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. https://t.co/SNLJWgrW6a https://t.co/Jpg…
vinberto: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
worldmelter: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
neorazorx: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
wedusk101: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
GilgameshNusku: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
Neku42: RT @arxiv_org: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. https://t.co/SNLJWgrW6a https://t.co/Jpg…
rakko8513: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
jdatmtjp: RT @arxiv_org: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. https://t.co/SNLJWgrW6a https://t.co/Jpg…
thundercomb: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
Osaraba_tokio: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
slwstr: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
HectorAnadon: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
Luigi_Di_Mise: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
sami3dat: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
Rets66: RT @arxiv_org: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. https://t.co/SNLJWgrW6a https://t.co/Jpg…
mondaru: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
_Farsash: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
4wmturner: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
cg_gadget: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
PriyanshJalan: RT @xsteenbrugge: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color Paper: https://t.co/xf9FZn7i3a Code:…
blauigris: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
APlaPi: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
SiavashSakhavi: RT @arxiv_org: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. https://t.co/SNLJWgrW6a https://t.co/Jpg…
Crybyte: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
hrsma2i: RT @arxiv_org: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. https://t.co/SNLJWgrW6a https://t.co/Jpg…
donamin: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
flatline29: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
KIL3RDEMON: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
allemagnus: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
Zahid_Akhtar: RT @arxiv_org: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. https://t.co/SNLJWgrW6a https://t.co/Jpg…
etcex: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
marsspider: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
jun40vn: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
m_de_giovanni: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
kodai_nakashima: RT @arxiv_org: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. https://t.co/SNLJWgrW6a https://t.co/Jpg…
theo_matussiere: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
taro_0718_: RT @arxiv_org: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. https://t.co/SNLJWgrW6a https://t.co/Jpg…
hjguyhan: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
_WizDom13_: RT @arxiv_org: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. https://t.co/SNLJWgrW6a https://t.co/Jpg…
linearlog: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
shubh_300595: RT @arxiv_org: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. https://t.co/SNLJWgrW6a https://t.co/Jpg…
dreizehnutters: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
lipsongsun: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
clintgt: RT @arxiv_org: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. https://t.co/SNLJWgrW6a https://t.co/Jpg…
_Ver__Che_: RT @arxiv_org: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. https://t.co/SNLJWgrW6a https://t.co/Jpg…
al_ro_tw: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
kara_oskara: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
ConsultComplex: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
BG_12121: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
atsuc: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
ZucchiFederico: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
TLecailtel: RT @xsteenbrugge: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color Paper: https://t.co/xf9FZn7i3a Code:…
ccanoespinosa: RT @arxiv_org: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. https://t.co/SNLJWgrW6a https://t.co/Jpg…
shvethasuvarna: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
haru_256: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
BennetotA: RT @xsteenbrugge: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color Paper: https://t.co/xf9FZn7i3a Code:…
SoyOscarRH: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
vfx_ai: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
ktrk15: RT @arxiv_org: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. https://t.co/SNLJWgrW6a https://t.co/Jpg…
Adrialvaro02: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
n_frazier_logue: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
_Dolshe: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
malharkamat: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
al_kaimiya: RT @arxiv_org: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. https://t.co/SNLJWgrW6a https://t.co/Jpg…
kuz44ma69: RT @arxiv_org: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. https://t.co/SNLJWgrW6a https://t.co/Jpg…
MkMdw: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
faustoandreses1: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
wallacexia1: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
ajzcopyno: RT @samim: SC-FEGAN: "Face Editing Generative Adversarial Network with User's Sketch and Color": https://t.co/7hQiCPlajc https://t.co/ZRJw…
qkisw: RT @arxiv_org: SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color. https://t.co/SNLJWgrW6a https://t.co/Jpg…
Github

SC-FEGAN : Face Editing Generative Adversarial Network with User's Sketch and Color

Repository: SC-FEGAN
User: JoYoungjoo
Language: Python
Stargazers: 568
Subscribers: 21
Forks: 75
Open Issues: 4
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None.
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Sample Sizes : None.
Authors: 2
Total Words: 4882
Unqiue Words: 1526

2.192 Mikeys
#2. GSLAM: A General SLAM Framework and Benchmark
Yong Zhao, Shibiao Xu, Shuhui Bu, Hongkai Jiang, Pengcheng Han
SLAM technology has recently seen many successes and attracted the attention of high-technological companies. However, how to unify the interface of existing or emerging algorithms, and effectively perform benchmark about the speed, robustness and portability are still problems. In this paper, we propose a novel SLAM platform named GSLAM, which not only provides evaluation functionality, but also supplies useful toolkit for researchers to quickly develop their own SLAM systems. The core contribution of GSLAM is an universal, cross-platform and full open-source SLAM interface for both research and commercial usage, which is aimed to handle interactions with input dataset, SLAM implementation, visualization and applications in an unified framework. Through this platform, users can implement their own functions for better performance with plugin form and further boost the application to practical usage of the SLAM.
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arxivml: "GSLAM: A General SLAM Framework and Benchmark", Yong Zhao, Shibiao Xu, Shuhui Bu, Hongkai Jiang, Pengcheng Han https://t.co/2VJkaNb9Gq
arxiv_cscv: GSLAM: A General SLAM Framework and Benchmark https://t.co/y3GBoeSzlh
arxiv_cscv: GSLAM: A General SLAM Framework and Benchmark https://t.co/y3GBoeSzlh
robotic_hands: RT @arxiv_cscv: GSLAM: A General SLAM Framework and Benchmark https://t.co/y3GBoeSzlh
udmrzn: RT @arxiv_cscv: GSLAM: A General SLAM Framework and Benchmark https://t.co/y3GBoeSzlh
KagamiSho: RT @arxiv_cscv: GSLAM: A General SLAM Framework and Benchmark https://t.co/y3GBoeSzlh
sumicco_cv: RT @arxiv_cscv: GSLAM: A General SLAM Framework and Benchmark https://t.co/y3GBoeSzlh
Github

A General Simultaneous Localization and Mapping Framework which supports feature based or direct method and different sensors including monocular camera, RGB-D sensors or any other input types can be handled.

Repository: GSLAM
User: zdzhaoyong
Language: C++
Stargazers: 163
Subscribers: 20
Forks: 49
Open Issues: 4
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None.
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Sample Sizes : None.
Authors: 5
Total Words: 7303
Unqiue Words: 2400

2.152 Mikeys
#3. Self-supervised Learning for Dense Depth Estimation in Monocular Endoscopy
Xingtong Liu, Ayushi Sinha, Masaru Ishii, Gregory D. Hager, Austin Reiter, Russell H. Taylor, Mathias Unberath
We present a self-supervised approach to training convolutional neural networks for dense depth estimation from monocular endoscopy data without a priori modeling of anatomy or shading. Our method only requires monocular endoscopic video and a multi-view stereo method, e.g. structure from motion, to supervise learning in a sparse manner. Consequently, our method requires neither manual labeling nor patient computed tomography (CT) scan in the training and application phases. In a cross-patient experiment using CT scans as groundtruth, the proposed method achieved submillimeter root mean squared error. In a comparison study to a recent self-supervised depth estimation method designed for natural video on in vivo sinus endoscopy data, we demonstrate that the proposed approach outperforms the previous method by a large margin. The source code for this work is publicly available online at https://github.com/lppllppl920/EndoscopyDepthEstimation-Pytorch.
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Figures
Tweets
arxiv_org: Self-supervised Learning for Dense Depth Estimation in Monocular Endoscopy. https://t.co/78dhN90dEB https://t.co/64gp6SH0GN
BrundageBot: Self-supervised Learning for Dense Depth Estimation in Monocular Endoscopy. Xingtong Liu, Ayushi Sinha, Masaru Ishii, Gregory D. Hager, Austin Reiter, Russell H. Taylor, and Mathias Unberath https://t.co/Y4cPPp9GPO
arxivml: "Self-supervised Learning for Dense Depth Estimation in Monocular Endoscopy", Xingtong Liu, Ayushi Sinha, Masaru Is… https://t.co/8Jcz3HjAO9
arxiv_cscv: Self-supervised Learning for Dense Depth Estimation in Monocular Endoscopy https://t.co/KLCIWlJSYO
arxiv_cscv: Self-supervised Learning for Dense Depth Estimation in Monocular Endoscopy https://t.co/KLCIWlJSYO
arxiv_cscv: Self-supervised Learning for Dense Depth Estimation in Monocular Endoscopy https://t.co/KLCIWlJSYO
udmrzn: RT @arxiv_org: Self-supervised Learning for Dense Depth Estimation in Monocular Endoscopy. https://t.co/78dhN90dEB https://t.co/64gp6SH0GN
Github

Self-supervised learning for dense depth estimation in monocular endoscopy

Repository: EndoscopyDepthEstimation-Pytorch
User: lppllppl920
Language: Python
Stargazers: 3
Subscribers: 1
Forks: 0
Open Issues: 0
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Sample Sizes : None.
Authors: 7
Total Words: 6215
Unqiue Words: 1914

2.149 Mikeys
#4. Random Search and Reproducibility for Neural Architecture Search
Liam Li, Ameet Talwalkar
Neural architecture search (NAS) is a promising research direction that has the potential to replace expert-designed networks with learned, task-specific architectures. In this work, in order to help ground the empirical results in this field, we propose new NAS baselines that build off the following observations: (i) NAS is a specialized hyperparameter optimization problem; and (ii) random search is a competitive baseline for hyperparameter optimization. Leveraging these observations, we evaluate both random search with early-stopping and a novel random search with weight-sharing algorithm on two standard NAS benchmarks---PTB and CIFAR-10. Our results show that random search with early-stopping is a competitive NAS baseline, e.g., it performs at least as well as ENAS, a leading NAS method, on both benchmarks. Additionally, random search with weight-sharing outperforms random search with early-stopping, achieving a state-of-the-art NAS result on PTB and a highly competitive result on CIFAR-10. Finally, we explore the existing...
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Figures
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Miles_Brundage: "Random Search and Reproducibility for Neural Architecture Search," Li and Talwalkar: https://t.co/Je9R9CIGXC
hardmaru: Random Search and Reproducibility for Neural Architecture Search They show that random search of architectures is a strong baseline for architecture search. In fact, random search gets near state-of-the-art results on PTB (RNNs) and CIFAR-10 (ConvNets). https://t.co/BrvrwhbkwS https://t.co/yctEhgagjk
BrundageBot: Random Search and Reproducibility for Neural Architecture Search. Liam Li and Ameet Talwalkar https://t.co/QXrkCcbFrI
mikepqr: lmao "Our results show that random search with early-stopping is a competitive NAS baseline, e.g., it performs at least as well as ENAS, a leading NAS method, on both benchmarks" https://t.co/A9Un0H0fid
anitayorker: 'Kenreisman/machine-learning' Top: [1902.07638] Random Search and Reproducibility for Neural Architecture Search https://t.co/kVhuxByTSR, see more https://t.co/VAU8peuLvN
arxivml: "Random Search and Reproducibility for Neural Architecture Search", Liam Li, Ameet Talwalkar https://t.co/k4XuyBqrvm
reddit_ml: [R] Random Search and Reproducibility for Neural Architecture Search: Random Search is surprisingly competitive w... https://t.co/lPeMlYRuaU
reddit_ml: [P] Random Search and Reproducibility for Neural Architecture Search https://t.co/VTWY4q2WCM
atalwalkar: @zacharylipton @zacharylipton we just wrote a paper on a very related topic: https://t.co/LYv6UW95SP
atalwalkar: It turns out that random search works really well for neural architecture search...https://t.co/LYv6UW95SP
hereticreader: Random Search and Reproducibility for Neural Architecture Search - https://t.co/9D1COPFWPY https://t.co/qizFCfCBBT
arxiv_cs_LG: Random Search and Reproducibility for Neural Architecture Search. Liam Li and Ameet Talwalkar https://t.co/5tj74tlcty
zacharylipton: RT @atalwalkar: @zacharylipton @zacharylipton we just wrote a paper on a very related topic: https://t.co/LYv6UW95SP
bigdata: RT @atalwalkar: It turns out that random search works really well for neural architecture search...https://t.co/LYv6UW95SP
pacoid: RT @atalwalkar: It turns out that random search works really well for neural architecture search...https://t.co/LYv6UW95SP
neil_conway: RT @atalwalkar: It turns out that random search works really well for neural architecture search...https://t.co/LYv6UW95SP
brandondamos: RT @Miles_Brundage: "Random Search and Reproducibility for Neural Architecture Search," Li and Talwalkar: https://t.co/Je9R9CIGXC
IgorCarron: RT @Miles_Brundage: "Random Search and Reproducibility for Neural Architecture Search," Li and Talwalkar: https://t.co/Je9R9CIGXC
ayirpelle: RT @Miles_Brundage: "Random Search and Reproducibility for Neural Architecture Search," Li and Talwalkar: https://t.co/Je9R9CIGXC
mandubian: RT @Miles_Brundage: "Random Search and Reproducibility for Neural Architecture Search," Li and Talwalkar: https://t.co/Je9R9CIGXC
HazyResearch: RT @atalwalkar: It turns out that random search works really well for neural architecture search...https://t.co/LYv6UW95SP
EricSchles: RT @Miles_Brundage: "Random Search and Reproducibility for Neural Architecture Search," Li and Talwalkar: https://t.co/Je9R9CIGXC
aneesha: RT @atalwalkar: It turns out that random search works really well for neural architecture search...https://t.co/LYv6UW95SP
dbeyer123: RT @atalwalkar: It turns out that random search works really well for neural architecture search...https://t.co/LYv6UW95SP
desertnaut: RT @Miles_Brundage: "Random Search and Reproducibility for Neural Architecture Search," Li and Talwalkar: https://t.co/Je9R9CIGXC
TweetAtAKK: RT @atalwalkar: It turns out that random search works really well for neural architecture search...https://t.co/LYv6UW95SP
elieraad: RT @atalwalkar: It turns out that random search works really well for neural architecture search...https://t.co/LYv6UW95SP
ajratner: RT @atalwalkar: It turns out that random search works really well for neural architecture search...https://t.co/LYv6UW95SP
pablete: RT @atalwalkar: It turns out that random search works really well for neural architecture search...https://t.co/LYv6UW95SP
evanrsparks: RT @atalwalkar: It turns out that random search works really well for neural architecture search...https://t.co/LYv6UW95SP
talkdatatomee: RT @Miles_Brundage: "Random Search and Reproducibility for Neural Architecture Search," Li and Talwalkar: https://t.co/Je9R9CIGXC
dkislyuk: RT @atalwalkar: It turns out that random search works really well for neural architecture search...https://t.co/LYv6UW95SP
yoavz_: RT @atalwalkar: It turns out that random search works really well for neural architecture search...https://t.co/LYv6UW95SP
brutforcimag: RT @Miles_Brundage: "Random Search and Reproducibility for Neural Architecture Search," Li and Talwalkar: https://t.co/Je9R9CIGXC
eaplatanios: RT @atalwalkar: It turns out that random search works really well for neural architecture search...https://t.co/LYv6UW95SP
agispof: RT @Miles_Brundage: "Random Search and Reproducibility for Neural Architecture Search," Li and Talwalkar: https://t.co/Je9R9CIGXC
nsdgpn: RT @atalwalkar: It turns out that random search works really well for neural architecture search...https://t.co/LYv6UW95SP
etherealkatana: RT @Miles_Brundage: "Random Search and Reproducibility for Neural Architecture Search," Li and Talwalkar: https://t.co/Je9R9CIGXC
drorhilman: RT @Miles_Brundage: "Random Search and Reproducibility for Neural Architecture Search," Li and Talwalkar: https://t.co/Je9R9CIGXC
scaldas_ml: RT @atalwalkar: It turns out that random search works really well for neural architecture search...https://t.co/LYv6UW95SP
Atalantahaze: RT @Miles_Brundage: "Random Search and Reproducibility for Neural Architecture Search," Li and Talwalkar: https://t.co/Je9R9CIGXC
Github

Code release for paper "Random Search and Reproducibility for NAS"

Repository: randomNAS_release
User: liamcli
Language: Python
Stargazers: 22
Subscribers: 1
Forks: 0
Open Issues: 0
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None.
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Sample Sizes : None.
Authors: 2
Total Words: 12436
Unqiue Words: 2614

2.149 Mikeys
#5. Finding the Needle in a Haystack: Detrending Photometric Timeseries Data of Strictly Periodic Astrophysical Objects
Andrej Prsa, Moses Zhang, Mark Wells
Light curves of astrophysical objects frequently contain strictly periodic signals. In those cases we can use that property to aid the detrending algorithm to fully disentangle an unknown periodic signal and an unknown baseline signal with no power at that period. The periodic signal is modeled as a discrete probability distribution function (pdf), while the baseline signal is modeled as a residual timeseries. Those two components are disentangled by minimizing the length of the residual timeseries w.r.t. the per-bin pdf fluxes. We demonstrate the use of the algorithm on a synthetic case, on the eclipsing binary KIC 3953981 and on the eccentric ellipsoidal variable KIC 3547874. We further discuss the parameters and the limitations of the algorithm and speculate on the two most common use cases: detrending the periodic signal of interest and measuring the dependence of instrumental response on controlled instrumental variables. A more sophisticated version of the algorithm is released as open source on github and available via pip.
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Tweets
warrickball: Spent a few minutes this morning throwing a @NASA_TESS lightcurve at the code in this paper by Prsa, Zhang & Wells on arXiv today, then noticed that one of Zhang's affiliations is Byram Hills *High School*. :-o https://t.co/4xaCFkIuc3
Github

A friendly package for Kepler & TESS time series analysis in Python.

Repository: lightkurve
User: KeplerGO
Language: Python
Stargazers: 79
Subscribers: 14
Forks: 50
Open Issues: 93
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None.
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Sample Sizes : None.
Authors: 3
Total Words: 5758
Unqiue Words: 1897

2.133 Mikeys
#6. Evaluating the Search Phase of Neural Architecture Search
Christian Sciuto, Kaicheng Yu, Martin Jaggi, Claudiu Musat, Mathieu Salzmann
Neural Architecture Search (NAS) aims to facilitate the design of deep networks for new tasks. Existing techniques rely on two stages: searching over the architecture space and validating the best architecture. Evaluating NAS algorithms is currently solely done by comparing their results on the downstream task. While intuitive, this fails to explicitly evaluate the effectiveness of their search strategies. In this paper, we extend the NAS evaluation procedure to include the search phase. To this end, we compare the quality of the solutions obtained by NAS search policies with that of random architecture selection. We find that: (i) On average, the random policy outperforms state-of-the-art NAS algorithms; and (ii) The results and candidate rankings of NAS algorithms do not reflect the true performance of the candidate architectures. While our former finding illustrates the fact that the NAS search space has been sufficiently constrained so that random solutions yield good results, we trace the latter back to the weight sharing...
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Miles_Brundage: "Evaluating the Search Phase of Neural Architecture Search," Sciuto and Yu et al.: https://t.co/xdzlQGLISz
BrundageBot: Evaluating the Search Phase of Neural Architecture Search. Christian Sciuto, Kaicheng Yu, Martin Jaggi, Claudiu Musat, and Mathieu Salzmann https://t.co/BMI3kDUO7J
arxivml: "Evaluating the Search Phase of Neural Architecture Search", Christian Sciuto, Kaicheng Yu, Martin Jaggi, Claudiu M… https://t.co/e9PfXDHep1
_Claudiu_Musat: @jeremyphoward Great work! We reached some similar conclusions and have some additional insights as to why this happened :https://t.co/PYtGpZX607
arxiv_cs_LG: Evaluating the Search Phase of Neural Architecture Search. Christian Sciuto, Kaicheng Yu, Martin Jaggi, Claudiu Musat, and Mathieu Salzmann https://t.co/hjM3Yfv0Xh
semiDL: RT @Miles_Brundage: "Evaluating the Search Phase of Neural Architecture Search," Sciuto and Yu et al.: https://t.co/xdzlQGLISz
alexdaviscmu: RT @Miles_Brundage: "Evaluating the Search Phase of Neural Architecture Search," Sciuto and Yu et al.: https://t.co/xdzlQGLISz
JeanMarcJAzzi: RT @Miles_Brundage: "Evaluating the Search Phase of Neural Architecture Search," Sciuto and Yu et al.: https://t.co/xdzlQGLISz
indy9000: RT @Miles_Brundage: "Evaluating the Search Phase of Neural Architecture Search," Sciuto and Yu et al.: https://t.co/xdzlQGLISz
blauigris: RT @Miles_Brundage: "Evaluating the Search Phase of Neural Architecture Search," Sciuto and Yu et al.: https://t.co/xdzlQGLISz
treasured_write: RT @Miles_Brundage: "Evaluating the Search Phase of Neural Architecture Search," Sciuto and Yu et al.: https://t.co/xdzlQGLISz
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Authors: 5
Total Words: 7028
Unqiue Words: 1861

2.132 Mikeys
#7. Probabilistic Neural-symbolic Models for Interpretable Visual Question Answering
Ramakrishna Vedantam, Karan Desai, Stefan Lee, Marcus Rohrbach, Dhruv Batra, Devi Parikh
We propose a new class of probabilistic neural-symbolic models, that have symbolic functional programs as a latent, stochastic variable. Instantiated in the context of visual question answering, our probabilistic formulation offers two key conceptual advantages over prior neural-symbolic models for VQA. Firstly, the programs generated by our model are more understandable while requiring lesser number of teaching examples. Secondly, we show that one can pose counterfactual scenarios to the model, to probe its beliefs on the programs that could lead to a specified answer given an image. Our results on the CLEVR and SHAPES datasets verify our hypotheses, showing that the model gets better program (and answer) prediction accuracy even in the low data regime, and allows one to probe the coherence and consistency of reasoning performed.
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arxiv_org: Probabilistic Neural-symbolic Models for Interpretable Visual Question Answering. https://t.co/llLfuAXB7p https://t.co/mTa36cadA4
BrundageBot: Probabilistic Neural-symbolic Models for Interpretable Visual Question Answering. Ramakrishna Vedantam, Karan Desai, Stefan Lee, Marcus Rohrbach, Dhruv Batra, and Devi Parikh https://t.co/SHJstPBQrl
arxivml: "Probabilistic Neural-symbolic Models for Interpretable Visual Question Answering", Ramakrishna Vedantam, Karan Des… https://t.co/DXGxa0VQJh
kdexd: Our paper "Probabilistic Neural-symbolic Models for Interpretable Visual Question Answering" is now on ArXiv (https://t.co/020tfSROHS )! w/ @vrama91, @stefmlee, Marcus Rohrbach, @DhruvBatraDB, @deviparikh. We propose a class of probabilistic models for symbolic reasoning ...(1/3)
arxiv_cs_LG: Probabilistic Neural-symbolic Models for Interpretable Visual Question Answering. Ramakrishna Vedantam, Karan Desai, Stefan Lee, Marcus Rohrbach, Dhruv Batra, and Devi Parikh https://t.co/48BEpmTXF0
arxiv_cscv: Probabilistic Neural-symbolic Models for Interpretable Visual Question Answering https://t.co/1j0q1R7sUA
arxiv_cscv: Probabilistic Neural-symbolic Models for Interpretable Visual Question Answering https://t.co/1j0q1R7sUA
puneethmishra: RT @arxiv_org: Probabilistic Neural-symbolic Models for Interpretable Visual Question Answering. https://t.co/llLfuAXB7p https://t.co/mTa36…
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Total Words: 10613
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2.123 Mikeys
#8. Perceptual Quality-preserving Black-Box Attack against Deep Learning Image Classifiers
Diego Gragnaniello, Francesco Marra, Giovanni Poggi, Luisa Verdoliva
Deep neural networks provide unprecedented performance in all image classification problems, leveraging the availability of huge amounts of data for training. Recent studies, however, have shown their vulnerability to adversarial attacks, spawning an intense research effort in this field. With the aim of building better systems, new countermeasures and stronger attacks are proposed by the day. On the attacker's side, there is growing interest for the realistic black-box scenario, in which the user has no access to the neural network parameters. The problem is to design limited-complexity attacks which mislead the neural network without impairing image quality too much, not to raise the attention of human observers. In this work, we put special emphasis on this latter requirement and propose a powerful and low-complexity black-box attack which preserves perceptual image quality. Numerical experiments prove the effectiveness of the proposed techniques both for tasks commonly considered in this context, and for other applications in...
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arxiv_org: Perceptual Quality-preserving Black-Box Attack against Deep Learning Image Classifiers. https://t.co/DtQ8bXm3tm https://t.co/qm6yvn1pne
BrundageBot: Perceptual Quality-preserving Black-Box Attack against Deep Learning Image Classifiers. Diego Gragnaniello, Francesco Marra, Giovanni Poggi, and Luisa Verdoliva https://t.co/cDlhE1ZVTo
arxivml: "Perceptual Quality-preserving Black-Box Attack against Deep Learning Image Classifiers", Diego Gragnaniello, Franc… https://t.co/cA9sFvZTzS
arxiv_cscv: Perceptual Quality-preserving Black-Box Attack against Deep Learning Image Classifiers https://t.co/zbTz4nSATs
arxiv_cscv: Perceptual Quality-preserving Black-Box Attack against Deep Learning Image Classifiers https://t.co/zbTz4nSATs
arxiv_cscv: Perceptual Quality-preserving Black-Box Attack against Deep Learning Image Classifiers https://t.co/zbTz4oabL0
arxiv_cscv: Perceptual Quality-preserving Black-Box Attack against Deep Learning Image Classifiers https://t.co/zbTz4nSATs
AlamHilaal: RT @arxiv_org: Perceptual Quality-preserving Black-Box Attack against Deep Learning Image Classifiers. https://t.co/DtQ8bXm3tm https://t.co…
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Total Words: 8380
Unqiue Words: 2666

2.12 Mikeys
#9. Predicting ConceptNet Path Quality Using Crowdsourced Assessments of Naturalness
Yilun Zhou, Steven Schockaert, Julie A. Shah
In many applications, it is important to characterize the way in which two concepts are semantically related. Knowledge graphs such as ConceptNet provide a rich source of information for such characterizations by encoding relations between concepts as edges in a graph. When two concepts are not directly connected by an edge, their relationship can still be described in terms of the paths that connect them. Unfortunately, many of these paths are uninformative and noisy, which means that the success of applications that use such path features crucially relies on their ability to select high-quality paths. In existing applications, this path selection process is based on relatively simple heuristics. In this paper we instead propose to learn to predict path quality from crowdsourced human assessments. Since we are interested in a generic task-independent notion of quality, we simply ask human participants to rank paths according to their subjective assessment of the paths' naturalness, without attempting to define naturalness or...
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arxiv_org: Predicting ConceptNet Path Quality Using Crowdsourced Assessments of Naturalness. https://t.co/O1W4cH94ai https://t.co/s4ESa24Zjb
arxivml: "Predicting ConceptNet Path Quality Using Crowdsourced Assessments of Naturalness", Yilun Zhou, Steven Schockaert, … https://t.co/AQfIT2BaTf
arxiv_cscl: Predicting ConceptNet Path Quality Using Crowdsourced Assessments of Naturalness https://t.co/HMPsboc4a1
arxiv_cscl: Predicting ConceptNet Path Quality Using Crowdsourced Assessments of Naturalness https://t.co/HMPsbnUsLr
arxiv_cscl: Predicting ConceptNet Path Quality Using Crowdsourced Assessments of Naturalness https://t.co/HMPsboc4a1
RexDouglass: RT @arxiv_cscl: Predicting ConceptNet Path Quality Using Crowdsourced Assessments of Naturalness https://t.co/HMPsbnUsLr
Github

Code for the paper "Predicting ConceptNet Path Quality Using Crowdsourced Assessments of Naturalness".

Repository: path-naturalness-prediction
User: YilunZhou
Language: Python
Stargazers: 0
Subscribers: 1
Forks: 0
Open Issues: 0
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Authors: 3
Total Words: 10526
Unqiue Words: 3142

2.103 Mikeys
#10. Learning Dual Retrieval Module for Semi-supervised Relation Extraction
Hongtao Lin, Jun Yan, Meng Qu, Xiang Ren
Relation extraction is an important task in structuring content of text data, and becomes especially challenging when learning with weak supervision---where only a limited number of labeled sentences are given and a large number of unlabeled sentences are available. Most existing work exploits unlabeled data based on the ideas of self-training (i.e., bootstrapping a model) and multi-view learning (e.g., ensembling multiple model variants). However, these methods either suffer from the issue of semantic drift, or do not fully capture the problem characteristics of relation extraction. In this paper, we leverage a key insight that retrieving sentences expressing a relation is a dual task of predicting relation label for a given sentence---two tasks are complementary to each other and can be optimized jointly for mutual enhancement. To model this intuition, we propose DualRE, a principled framework that introduces a retrieval module which is jointly trained with the original relation prediction module. In this way, high-quality...
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BrundageBot: Learning Dual Retrieval Module for Semi-supervised Relation Extraction. Hongtao Lin, Jun Yan, Meng Qu, and Xiang Ren https://t.co/vgV1BXMvlA
arxivml: "Learning Dual Retrieval Module for Semi-supervised Relation Extraction", Hongtao Lin, Jun Yan, Meng Qu, Xiang Ren https://t.co/cuynnUIVH9
arxiv_cscl: Learning Dual Retrieval Module for Semi-supervised Relation Extraction https://t.co/vYBiGwCQke
arxiv_cscl: Learning Dual Retrieval Module for Semi-supervised Relation Extraction https://t.co/vYBiGwCQke
udmrzn: RT @arxiv_cscl: Learning Dual Retrieval Module for Semi-supervised Relation Extraction https://t.co/vYBiGwCQke
Github
Repository: DualRE
User: INK-USC
Language: Python
Stargazers: 2
Subscribers: 2
Forks: 0
Open Issues: 0
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Sample Sizes : None.
Authors: 4
Total Words: 10792
Unqiue Words: 3037

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