Producing diverse and realistic images with generative models such as GANs typically requires large scale training with vast amount of images. GANs trained with extremely limited data can easily overfit to few training samples and display undesirable properties like "stairlike" latent space where transitions in latent space suffer from discontinuity, occasionally yielding abrupt changes in outputs. In this work, we consider the situation where neither large scale dataset of our interest nor transferable source dataset is available, and seek to train existing generative models with minimal overfitting and mode collapse. We propose latent mixup-based distance regularization on the feature space of both a generator and the counterpart discriminator that encourages the two players to reason not only about the scarce observed data points but the relative distances in the feature space they reside. Qualitative and quantitative evaluation on diverse datasets demonstrates that our method is generally applicable to existing models to enhance both fidelity and diversity under the constraint of limited data. Code will be made public.

本文提出了一种基于特征空间上的混合距离正则化的方法，用于训练现有的生成模型，以在少量样本的情况下增强其逼真性和多样性，从而解决GANs在训练数据不足时的过拟合问题和模式崩溃现象。

基于Mixup距离学习的少样本图像生成