Exploring DeshuffleGANs in Self-Supervised Generative Adversarial Networks

11/03/2020
by   Gulcin Baykal, et al.
0

Generative Adversarial Networks (GANs) have become the most used network models towards solving the problem of image generation. In recent years, self-supervised GANs are proposed to aid stabilized GAN training without the catastrophic forgetting problem and to improve the image generation quality without the need for the class labels of the data. However, the generalizability of the self-supervision tasks on different GAN architectures is not studied before. To that end, we extensively analyze the contribution of the deshuffling task of DeshuffleGANs in the generalizability context. We assign the deshuffling task to two different GAN discriminators and study the effects of the deshuffling on both architectures. We also evaluate the performance of DeshuffleGANs on various datasets that are mostly used in GAN benchmarks: LSUN-Bedroom, LSUN-Church, and CelebA-HQ. We show that the DeshuffleGAN obtains the best FID results for LSUN datasets compared to the other self-supervised GANs. Furthermore, we compare the deshuffling with the rotation prediction that is firstly deployed to the GAN training and demonstrate that its contribution exceeds the rotation prediction. Lastly, we show the contribution of the self-supervision tasks to the GAN training on loss landscape and present that the effects of the self-supervision tasks may not be cooperative to the adversarial training in some settings. Our code can be found at https://github.com/gulcinbaykal/DeshuffleGAN.

READ FULL TEXT

page 16

page 19

research
10/19/2020

LT-GAN: Self-Supervised GAN with Latent Transformation Detection

Generative Adversarial Networks (GANs) coupled with self-supervised task...
research
06/15/2020

DeshuffleGAN: A Self-Supervised GAN to Improve Structure Learning

Generative Adversarial Networks (GANs) triggered an increased interest i...
research
11/06/2022

Distilling Representations from GAN Generator via Squeeze and Span

In recent years, generative adversarial networks (GANs) have been an act...
research
07/10/2016

Adversarial Training For Sketch Retrieval

Generative Adversarial Networks (GAN) are able to learn excellent repres...
research
06/26/2023

A Simple and Effective Baseline for Attentional Generative Adversarial Networks

Synthesising a text-to-image model of high-quality images by guiding the...
research
10/27/2018

Self-Supervised GAN to Counter Forgetting

GANs involve training two networks in an adversarial game, where each ne...
research
02/12/2021

Efficient Conditional GAN Transfer with Knowledge Propagation across Classes

Generative adversarial networks (GANs) have shown impressive results in ...

Please sign up or login with your details

Forgot password? Click here to reset