Contrastive Fine-grained Class Clustering via Generative Adversarial Networks

Unsupervised fine-grained class clustering is practical yet challenging task due to the difficulty of feature representations learning of subtle object details. We introduce C3-GAN, a method that leverages the categorical inference power of InfoGAN by applying contrastive learning. We aim to learn feature representations that encourage the data to form distinct cluster boundaries in the embedding space, while also maximizing the mutual information between the latent code and its observation. Our approach is to train the discriminator, which is used for inferring clusters, to optimize the contrastive loss, where the image-latent pairs that maximize the mutual information are considered as positive pairs and the rest as negative pairs. Specifically, we map the input of the generator, which has sampled from the categorical distribution, to the embedding space of the discriminator and let them act as a cluster centroid. In this way, C3-GAN achieved to learn a clustering-friendly embedding space where each cluster is distinctively separable. Experimental results show that C3-GAN achieved state-of-the-art clustering performance on four fine-grained benchmark datasets, while also alleviating the mode collapse phenomenon.