Unsupervised Minimax: Adversarial Curiosity, Generative Adversarial Networks, and Predictability Minimization
Generative Adversarial Networks (GANs) learn to model data distributions through two unsupervised neural networks, each minimizing the objective function maximized by the other. We relate this game theoretic strategy to earlier neural networks playing unsupervised minimax games. (i) GANs can be formulated as a special case of Adversarial Curiosity (1990) based on a minimax duel between two networks, one generating data through its probabilistic actions, the other predicting consequences thereof. (ii) We correct a previously published claim that Predictability Minimization (PM, 1990s) is not based on a minimax game. PM models data distributions through a neural encoder that maximizes the objective function minimized by a neural predictor of the code components.
NurtureToken New!

Token crowdsale for this paper ends in

Buy Nurture Tokens

Author

Are you an author of this paper? Check the Twitter handle we have for you is correct.

Juergen Schmidhuber (add twitter)
Ask The Authors

Ask the authors of this paper a question or leave a comment.

Read it. Rate it.
#1. Which part of the paper did you read?

#2. The paper contains new data or analyses that is openly accessible?
#3. The conclusion is supported by the data and analyses?
#4. The conclusion is of scientific interest?
#5. The result is likely to lead to future research?

Github
User:
None (add)
Repo:
None (add)
Stargazers:
0
Forks:
0
Open Issues:
0
Network:
0
Subscribers:
0
Language:
None
Youtube
Link:
None (add)
Views:
0
Likes:
0
Dislikes:
0
Favorites:
0
Comments:
0
Other
Sample Sizes (N=):
Inserted:
Words Total:
Words Unique:
Source:
Abstract:
None
06/11/19 06:03PM
7,367
2,566
Tweets
hereticreader: Unsupervised Minimax: Adversarial Curiosity, Generative Adversarial Networks, and Predictability Minimization https://t.co/5fZwFgXm6x
therealjpittman: [R] [1906.04493] Unsupervised Minimax: Adversarial Curiosity, Generative Adversarial Networks, and Predictability Minimization (Schmidhuber) https://t.co/bPPZpn2bfI #MachineLearning
miguelgfierro: He is back https://t.co/PH6QM8S7FX https://t.co/kawj3IesL3
Memoirs: Unsupervised Minimax: Adversarial Curiosity, Generative Adversarial Networks, and Predictability Minimization. https://t.co/eqoDqlDB1Z
hardmaru: Unsupervised Minimax: Adversarial Curiosity, Generative Adversarial Networks, and Predictability Minimization A new review paper by Schmidhuber linking GANs to earlier ideas about unsupervised minimax games. Might be useful for those looking beyond GANs. https://t.co/qruwyeppJb https://t.co/ToYKjci1M8
arxiv_cs_LG: Unsupervised Minimax: Adversarial Curiosity, Generative Adversarial Networks, and Predictability Minimization. Juergen Schmidhuber https://t.co/70FIcYm4HJ
reddit_ml: [R] [1906.04493] Unsupervised Minimax: Adversarial Curiosity, Generative Adversarial Networks, and Predictability... https://t.co/8SGQI7nITN
Images
Related