Despite the extensive research on training generative adversarial networks
(GANs) with limited training data, learning to generate images from long-tailed
training distributions remains fairly unexplored. In the presence of imbalanced
multi-class training data, GANs tend to favor classes with more samples,
leading to the generation of low-quality and less diverse samples in tail
classes. In this study, we aim to improve the training of class-conditional
GANs with long-tailed data. We propose a straightforward yet effective method
for knowledge sharing, allowing tail classes to borrow from the rich
information from classes with more abundant training data. More concretely, we
propose modifications to existing class-conditional GAN architectures to ensure
that the lower-resolution layers of the generator are trained entirely
unconditionally while reserving class-conditional generation for the
higher-resolution layers. Experiments on several long-tail benchmarks and GAN
architectures demonstrate a significant improvement over existing methods in
both the diversity and fidelity of the generated images. The code is available
at this https URL

我们提出了一种针对长尾数据的类条件生成对抗网络 (class-conditional GANs) 的训练改进方法，通过知识共享使尾部类别能够借用丰富信息的优秀类别，实验证明在生成图像的多样性和质量方面相较于现有方法有显著提升。