Disentangling and Learning Robust Representations with Natural Clustering

By Javier Antorán, Antonio Miguel. 2019

19 citations recorded by the source corpus.

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 which is capable of separating factors of variation which are exclusive to certain classes from factors that are shared among classes. This model implements an explicitly compositional latent variable structure by defining a class-conditioned latent space and a shared latent space. We show its usefulness for detecting and disentangling class-dependent generative factors as well as its capacity to generate artificial samples which contain characteristics unseen in the training data.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing

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Citations in the corpus

Getting a CLUE: A Method for Explaining Uncertainty Estimates (2020, background): “However, this brings forth another well-known issue with VAEs: distribution mismatch [43; 46; 47].”