ContraGAN: Contrastive Learning for Conditional Image Generation

TL;DR

ContraGAN introduces a contrastive learning approach for conditional image generation, leveraging relations between multiple images and labels to improve diversity and realism, outperforming existing models on benchmark datasets.

Contribution

It proposes a novel contrastive loss that considers data-to-data and data-to-class relations, enhancing conditional GAN performance.

Findings

Outperforms state-of-the-art models on Tiny ImageNet and ImageNet datasets.

Contrastive learning reduces discriminator overfitting.

Re-implemented 12 SOTA GANs for fair comparison.

Abstract

Conditional image generation is the task of generating diverse images using class label information. Although many conditional Generative Adversarial Networks (GAN) have shown realistic results, such methods consider pairwise relations between the embedding of an image and the embedding of the corresponding label (data-to-class relations) as the conditioning losses. In this paper, we propose ContraGAN that considers relations between multiple image embeddings in the same batch (data-to-data relations) as well as the data-to-class relations by using a conditional contrastive loss. The discriminator of ContraGAN discriminates the authenticity of given samples and minimizes a contrastive objective to learn the relations between training images. Simultaneously, the generator tries to generate realistic images that deceive the authenticity and have a low contrastive loss. The experimental results show that ContraGAN outperforms state-of-the-art-models by 7.3% and 7.7% on Tiny ImageNet and ImageNet datasets, respectively. Besides, we experimentally demonstrate that contrastive learning helps to relieve the overfitting of the discriminator. For a fair comparison, we re-implement twelve state-of-the-art GANs using the PyTorch library. The software package is available at https://github.com/POSTECH-CVLab/PyTorch-StudioGAN.