Label Augmentation As Inter-Class Data Augmentation for Conditional Image Synthesis With Imbalanced Data

Conditional image synthesis performs admirably when trained on well-constructed and balanced datasets. However, in practice, training datasets frequently contain minorities (i.e., a class with a few samples), known as imbalanced data, which causes difficulties in learning generative models. To address conditional image synthesis with imbalanced data, we analyze a diversity issue of label-preserving data augmentation and an affinity issue of non-label-preserving data augmentation. From this observation, we present label augmentation, which works as inter-class data augmentation that effectively augments data by predicting a new label for a given image using the prediction of a pretrained image classification model (i.e., probabilities for each class). We incorporate our label augmentation into the discriminator of a seminal conditional generative adversarial network (GAN) model, proposing Softlabel-GAN. Using class probabilities extracts class-invariant and shared features between similar classes, achieving data augmentation with high affinity and diversity. Our experiments on imbalanced datasets show that Softlabel-GAN produces images with high quality and diversity while being hardly affected by the number of samples in each class. Code: https://github.com/raven38/softlabel-gan.