Theoretical Analysis of Image-to-Image Translation with Adversarial Learning

Xudong Pan, Mi Zhang, Daizong Ding
Proceedings of the 35th International Conference on Machine Learning, PMLR 80:4006-4015, 2018.

Abstract

Recently, a unified model for image-to-image translation tasks within adversarial learning framework has aroused widespread research interests in computer vision practitioners. Their reported empirical success however lacks solid theoretical interpretations for its inherent mechanism. In this paper, we reformulate their model from a brand-new geometrical perspective and have eventually reached a full interpretation on some interesting but unclear empirical phenomenons from their experiments. Furthermore, by extending the definition of generalization for generative adversarial nets to a broader sense, we have derived a condition to control the generalization capability of their model. According to our derived condition, several practical suggestions have also been proposed on model design and dataset construction as a guidance for further empirical researches.

Cite this Paper

BibTeX
@InProceedings{pmlr-v80-pan18c, title = {Theoretical Analysis of Image-to-Image Translation with Adversarial Learning}, author = {Pan, Xudong and Zhang, Mi and Ding, Daizong}, booktitle = {Proceedings of the 35th International Conference on Machine Learning}, pages = {4006--4015}, year = {2018}, editor = {Dy, Jennifer and Krause, Andreas}, volume = {80}, series = {Proceedings of Machine Learning Research}, month = {10--15 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v80/pan18c/pan18c.pdf}, url = {https://proceedings.mlr.press/v80/pan18c.html}, abstract = {Recently, a unified model for image-to-image translation tasks within adversarial learning framework has aroused widespread research interests in computer vision practitioners. Their reported empirical success however lacks solid theoretical interpretations for its inherent mechanism. In this paper, we reformulate their model from a brand-new geometrical perspective and have eventually reached a full interpretation on some interesting but unclear empirical phenomenons from their experiments. Furthermore, by extending the definition of generalization for generative adversarial nets to a broader sense, we have derived a condition to control the generalization capability of their model. According to our derived condition, several practical suggestions have also been proposed on model design and dataset construction as a guidance for further empirical researches.} }
Endnote
%0 Conference Paper %T Theoretical Analysis of Image-to-Image Translation with Adversarial Learning %A Xudong Pan %A Mi Zhang %A Daizong Ding %B Proceedings of the 35th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2018 %E Jennifer Dy %E Andreas Krause %F pmlr-v80-pan18c %I PMLR %P 4006--4015 %U https://proceedings.mlr.press/v80/pan18c.html %V 80 %X Recently, a unified model for image-to-image translation tasks within adversarial learning framework has aroused widespread research interests in computer vision practitioners. Their reported empirical success however lacks solid theoretical interpretations for its inherent mechanism. In this paper, we reformulate their model from a brand-new geometrical perspective and have eventually reached a full interpretation on some interesting but unclear empirical phenomenons from their experiments. Furthermore, by extending the definition of generalization for generative adversarial nets to a broader sense, we have derived a condition to control the generalization capability of their model. According to our derived condition, several practical suggestions have also been proposed on model design and dataset construction as a guidance for further empirical researches.
APA
Pan, X., Zhang, M. & Ding, D.. (2018). Theoretical Analysis of Image-to-Image Translation with Adversarial Learning. Proceedings of the 35th International Conference on Machine Learning, in Proceedings of Machine Learning Research 80:4006-4015 Available from https://proceedings.mlr.press/v80/pan18c.html.

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