Unpaired Image Translation via Adaptive Convolution-based Normalization
Disentangling content and style information of an image has played an important role in recent success in image translation. In this setting, how to inject given style into an input image containing its own content is an important issue, but existing methods followed relatively simple approaches, leaving room for improvement especially when incorporating significant style changes. In response, we propose an advanced normalization technique based on adaptive convolution (AdaCoN), in order to properly impose style information into the content of an input image. In detail, after locally standardizing the content representation in a channel-wise manner, AdaCoN performs adaptive convolution where the convolution filter weights are dynamically estimated using the encoded style representation. The flexibility of AdaCoN can handle complicated image translation tasks involving significant style changes. Our qualitative and quantitative experiments demonstrate the superiority of our proposed method against various existing approaches that inject the style into the content.
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Wonwoong Cho (add twitter)
Kangyeol Kim (edit)
Eungyeup Kim (add twitter)
Hyunwoo J. Kim (add twitter)
Jaegul Choo (add twitter)
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12/01/19 06:02PM
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arxiv_pop: 2019/11/29 投稿 4位 CV(Computer Vision and Pattern Recognition) Unpaired Image Translation via Adaptive Convolution-based Normalization https://t.co/G22lfx3Fkq 11 Tweets 9 Retweets 36 Favorites
arxiv_in_review: #NeurIPS2019 Unpaired Image Translation via Adaptive Convolution-based Normalization. (arXiv:1911.13271v1 [cs\.CV]) https://t.co/h1hQ1Btaxc
UkiwhY: RT @roadrunning01: Unpaired Image Translation via Adaptive Convolution-based Normalization pdf: https://t.co/EIMnKJ2to6 abs: https://t.co/P…
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nick_debu_p: Unpaired Image Translation via Adaptive Convolution-based Normalization https://t.co/jYeTmZz7iw ペアなし画像変換分野,AdaIN以降すごいな
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arxiv_cs_LG: Unpaired Image Translation via Adaptive Convolution-based Normalization. Wonwoong Cho, Kangyeol Kim, Eungyeup Kim, Hyunwoo J. Kim, and Jaegul Choo https://t.co/CJS2HuSGd2
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