Efficient Distribution Matching of Representations via Noise-Injected Deep InfoMax

Ivan Butakov, Alexander Semenenko, Alexander Tolmachev, Andrey Gladkov, Marina Munkhoeva, Alexey Frolov

Keywords: information theory, deep infomax, self-supervised learning, representation learning, distribution matching, Gaussian embeddings

TL;DR: This paper enhances Deep InfoMax approach to self-supervised representation learning via the injection of additive independent noise, allowing for the representations conforming to specific distributions to be learned.

Abstract: Deep InfoMax (DIM) is a well-established method for self-supervised representation learning (SSRL) based on maximization of the mutual information between the input and the output of a deep neural network encoder. Despite the DIM and contrastive SSRL in general being well-explored, the task of learning representations conforming to a specific distribution (i.e., distribution matching, DM) is still under-addressed. Motivated by the importance of DM to several downstream tasks (including generative modeling, disentanglement, outliers detection and other), we enhance DIM to enable automatic matching of learned representations to a selected prior distribution. To achieve this, we propose injecting an independent noise into the normalized outputs of the encoder, while keeping the same InfoMax training objective. We show that such modification allows for learning uniformly and normally distributed representations, as well as representations of other absolutely continuous distributions. Our approach is tested on various downstream tasks. The results indicate a moderate trade-off between the performance on the downstream tasks and quality of DM.

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

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Submission Number: 6892