Joint moment-matching autoencoders

Image transformation between multiple domains has become a challenging problem in deep generative networks. This is because, in real-world applications, finding paired images in different domains is an expensive and impractical task. This paper proposes a new model named joint moment-matching autoencoders(JMA). This model learns to perform cross-domain transformation over multiple domains based on perceptual loss and maximum mean discrepancy criteria, in the absence of any paired images between the domains. Our results show that the proposed JMA framework successfully learns to transform images between domains without any paired data. We demonstrate that our model has good performance in the generative context as well as in the domain transformation tasks with better computational efficiency than conventional methods.

[1]  Ming-Yu Liu,et al.  Coupled Generative Adversarial Networks , 2016, NIPS.

[2]  Xiaogang Wang,et al.  Deep Learning Face Attributes in the Wild , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).

[3]  Bernhard Schölkopf,et al.  A Kernel Two-Sample Test , 2012, J. Mach. Learn. Res..

[4]  Léon Bottou,et al.  Towards Principled Methods for Training Generative Adversarial Networks , 2017, ICLR.

[5]  Sebastian Nowozin,et al.  f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization , 2016, NIPS.

[6]  Jian Sun,et al.  Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[7]  Leon A. Gatys,et al.  Texture Synthesis Using Convolutional Neural Networks , 2015, NIPS.

[8]  Graham W. Taylor,et al.  Deconvolutional networks , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[9]  Richard S. Zemel,et al.  Generative Moment Matching Networks , 2015, ICML.

[10]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[11]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[12]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[13]  Minho Lee,et al.  Generative Moment Matching Autoencoder with Perceptual Loss , 2017, ICONIP.

[14]  Bernhard Schölkopf,et al.  A Kernel Method for the Two-Sample-Problem , 2006, NIPS.

[15]  Yoshua Bengio,et al.  Better Mixing via Deep Representations , 2012, ICML.

[16]  Minho Lee,et al.  Coupled generative adversarial stacked Auto-encoder: CoGASA , 2018, Neural Networks.

[17]  Bernt Schiele,et al.  Generative Adversarial Text to Image Synthesis , 2016, ICML.