FakeCLR: Exploring Contrastive Learning for Solving Latent Discontinuity in Data-Efficient GANs

• URL: http://arxiv.org/abs/2207.08630v2
• Date: Tue, 19 Jul 2022 01:55:10 GMT
• Title: FakeCLR: Exploring Contrastive Learning for Solving Latent Discontinuity in Data-Efficient GANs
• Authors: Ziqiang Li, Chaoyue Wang, Heliang Zheng, Jing Zhang, Bin Li
• Abstract summary: Data-Efficient GANs (DE-GANs) aim to learn generative models with a limited amount of training data. Contrastive learning has shown the great potential of increasing the synthesis quality of DE-GANs. We propose FakeCLR, which only applies contrastive learning on fake samples.
• Score: 24.18718734850797
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data-Efficient GANs (DE-GANs), which aim to learn generative models with a limited amount of training data, encounter several challenges for generating high-quality samples. Since data augmentation strategies have largely alleviated the training instability, how to further improve the generative performance of DE-GANs becomes a hotspot. Recently, contrastive learning has shown the great potential of increasing the synthesis quality of DE-GANs, yet related principles are not well explored. In this paper, we revisit and compare different contrastive learning strategies in DE-GANs, and identify (i) the current bottleneck of generative performance is the discontinuity of latent space; (ii) compared to other contrastive learning strategies, Instance-perturbation works towards latent space continuity, which brings the major improvement to DE-GANs. Based on these observations, we propose FakeCLR, which only applies contrastive learning on perturbed fake samples, and devises three related training techniques: Noise-related Latent Augmentation, Diversity-aware Queue, and Forgetting Factor of Queue. Our experimental results manifest the new state of the arts on both few-shot generation and limited-data generation. On multiple datasets, FakeCLR acquires more than 15% FID improvement compared to existing DE-GANs. Code is available at https://github.com/iceli1007/FakeCLR.

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