Improving ClusterGAN Using Self-Augmented Information Maximization of Disentangling Latent Spaces

Abstract

Since their introduction in the last few years, conditional generative models have seen remarkable achievements. However, they often need the use of large amounts of labelled information. By using unsupervised conditional generation in conjunction with a clustering inference network, ClusterGAN has recently been able to achieve impressive clustering results. Since the real conditional distribution of data is ignored, the clustering inference network can only achieve inferior clustering performance by considering only uniform prior based generative samples. However, the true distribution is not necessarily balanced. Consequently, ClusterGAN fails to produce all modes, which results in sub-optimal clustering inference network performance. So, it is important to learn the prior, which tries to match the real distribution in an unsupervised way. In this paper, we propose self-augmentation information maximization improved ClusterGAN (SIMI-ClusterGAN) to learn the distinctive priors from the data directly. The proposed SIMI-ClusterGAN consists of four deep neural networks: self-augmentation prior network, generator, discriminator and clustering inference network. The proposed method has been validated using seven benchmark data sets and has shown improved performance over state-of-the art methods. To demonstrate the superiority of SIMI-ClusterGAN performance on imbalanced dataset, we have discussed two imbalanced conditions on MNIST datasets with one-class imbalance and three classes imbalanced cases. The results highlight the advantages of SIMI-ClusterGAN.

References in corpus (11)

• Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
• Disentangling by Factorising
• Recent Advances in Autoencoder-Based Representation Learning
• Distributional Smoothing with Virtual Adversarial Training
• Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks
• Learning Discrete Representations via Information Maximizing Self-Augmented Training
• Disentangling factors of variation in deep representations using adversarial training
• SpectralNet: Spectral Clustering using Deep Neural Networks
• Precise Recovery of Latent Vectors from Generative Adversarial Networks
• Multi-Channel Attention Selection GANs for Guided Image-to-Image Translation
• Effect of The Latent Structure on Clustering with GANs