AE-OT-GAN: Training GANs from data specific latent distribution

2020/01/11 by Dongsheng An, Yang Guo, An, Dongsheng +11 · 1 citation
Computer Science · #Advanced Image Processing Techniques #Computer Vision and Pattern Recognition (cs.CV) #Digital Media Forensic Detection #FOS: Computer and information sciences #FOS: Electrical engineering #Generative Adversarial Networks and Image Synthesis #Image and Video Processing (eess.IV) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2001.03698

openalex publication_date 2020/01/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Though generative adversarial networks (GANs) areprominent models to generate realistic and crisp images,they often encounter the mode collapse problems and arehard to train, which comes from approximating the intrinsicdiscontinuous distribution transform map with continuousDNNs. The recently proposed AE-OT model addresses thisproblem by explicitly computing the discontinuous distribu-tion transform map through solving a semi-discrete optimaltransport (OT) map in the latent space of the autoencoder.However the generated images are blurry. In this paper, wepropose the AE-OT-GAN model to utilize the advantages ofthe both models: generate high quality images and at thesame time overcome the mode collapse/mixture problems.Specifically, we first faithfully embed the low dimensionalimage manifold into the latent space by training an autoen-coder (AE). Then we compute the optimal transport (OT)map that pushes forward the uniform distribution to the la-tent distribution supported on the latent manifold. Finally,our GAN model is trained to generate high quality imagesfrom the latent distribution, the distribution transform mapfrom which to the empirical data distribution will be con-tinuous. The paired data between the latent code and thereal images gives us further constriction about the generator.Experiments on simple MNIST dataset and complex datasetslike Cifar-10 and CelebA show the efficacy and efficiency ofour proposed method.

Citations

• NICE: Non-linear Independent Components Estimation
• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Importance Weighted Autoencoders
• Stochastic Backpropagation and Approximate Inference in Deep Generative\n Models
• NIPS 2016 Tutorial: Generative Adversarial Networks
• Large Scale GAN Training for High Fidelity Natural Image Synthesis
• InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
• Conditional Image Generation with PixelCNN Decoders
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• Neural Discrete Representation Learning
• MaskGAN: Towards Diverse and Interactive Facial Image Manipulation
• Learning Multiple Layers of Features from Tiny Images

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