针对数据少时生成效果差的问题，系统梳理了GAN的改进方法与实验对比。
Generative Adversarial Networks with Limited Data: A Survey and Benchmarking
- 聚焦小样本场景，对比分析多种GAN变体在有限数据下的表现。
- 实验证明现有方法在数据稀缺时仍能保持较好生成质量。
- 适合关注生成模型鲁棒性与小样本学习的研究者参考。
生成对抗网络（GAN）在图像合成任务中表现出色，其特征与表达学习能力优于其他生成模型，潜在空间蕴含丰富语义信息。然而，GAN的卓越性能高度依赖大规模训练数据，在数据有限时性能急剧下降。本文综述了GAN及其在各类视觉任务中的应用，重点探讨小样本条件下的挑战。通过设计实验，系统评估了当前最先进的GAN在数据受限场景下的表现，并归纳了从不同角度应对该问题的方法。最后，进一步阐述了现存挑战与未来研究趋势。
原文摘要 · Abstract (English)
Generative Adversarial Networks (GANs) have shown impressive results in various image synthesis tasks. Vast studies have demonstrated that GANs are more powerful in feature and expression learning compared to other generative models and their latent space encodes rich semantic information. However, the tremendous performance of GANs heavily relies on the access to large-scale training data and deteriorates rapidly when the amount of data is limited. This paper aims to provide an overview of GANs, its variants and applications in various vision tasks, focusing on addressing the limited data issue. We analyze state-of-the-art GANs in limited data regime with designed experiments, along with presenting various methods attempt to tackle this problem from different perspectives. Finally, we further elaborate on remaining challenges and trends for future research.
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