On Mutual Information Maximization for Representation Learning

ICLR 2020 Michael TschannenJosip DjolongaPaul K. RubensteinSylvain GellyMario Lucic

Many recent methods for unsupervised or self-supervised representation learning train feature extractors by maximizing an estimate of the mutual information (MI) between different views of the data. This comes with several immediate problems: For example, MI is notoriously hard to estimate, and using it as an objective for representation learning may lead to highly entangled representations due to its invariance under arbitrary invertible transformations... (read more)

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