Disentangling and Learning Robust Representations with Natural Clustering
Disentangling and Learning Robust Representations with Natural Clustering
Learning representations that disentangle the underlying factors of variability in data is an intuitive way to achieve generalization in deep models. In this work, we address the scenario where generative factors present a multimodal distribution due to the existence of class distinction in the data. We propose N-VAE, a model …