DeepAI AI Chat
Log In Sign Up

Information Compensation for Deep Conditional Generative Networks

01/23/2020
by   Zehao Wang, et al.
50

In recent years, unsupervised/weakly-supervised conditional generative adversarial networks (GANs) have achieved many successes on the task of modeling and generating data. However, one of their weaknesses lies in their poor ability to separate, or disentangle, the different factors that characterize the representation encoded in their latent space. To address this issue, we propose a novel structure for unsupervised conditional GANs powered by a novel Information Compensation Connection (IC-Connection). The proposed IC-Connection enables GANs to compensate for information loss incurred during deconvolution operations. In addition, to quantify the degree of disentanglement on both discrete and continuous latent variables, we design a novel evaluation procedure. Our empirical results suggest that our method achieves better disentanglement compared to the state-of-the-art GANs in a conditional generation setting.

READ FULL TEXT

page 1

page 5

page 7

09/10/2018

ClusterGAN : Latent Space Clustering in Generative Adversarial Networks

Generative Adversarial networks (GANs) have obtained remarkable success ...
06/09/2021

Stein Latent Optimization for GANs

Generative adversarial networks (GANs) with clustered latent spaces can ...
11/19/2016

Invertible Conditional GANs for image editing

Generative Adversarial Networks (GANs) have recently demonstrated to suc...
10/05/2022

ciDATGAN: Conditional Inputs for Tabular GANs

Conditionality has become a core component for Generative Adversarial Ne...
06/11/2020

Conditional Sampling With Monotone GANs

We present a new approach for sampling conditional measures that enables...
04/17/2023

Bridging Discrete and Backpropagation: Straight-Through and Beyond

Backpropagation, the cornerstone of deep learning, is limited to computi...
01/15/2018

Unsupervised Cipher Cracking Using Discrete GANs

This work details CipherGAN, an architecture inspired by CycleGAN used f...