Learning Discrete Structured Representations by Adversarially Maximizing Mutual Information

Part of Proceedings of the International Conference on Machine Learning 1 pre-proceedings (ICML 2020)

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Karl Stratos, Sam Wiseman


We propose learning discrete structured representations from unlabeled data by maximizing the mutual information between a structured latent variable and a target variable. Calculating mutual information is intractable in this setting. Our key technical contribution is an adversarial objective that can be used to tractably estimate mutual information assuming only the feasibility of cross entropy calculation. We develop a concrete realization of this general formulation with Markov distributions over binary encodings. We report critical and unexpected findings on practical aspects of the objective such as the choice of variational priors. We apply our model on document hashing and show that it outperforms current best baselines based on straight-through estimators and vector quantization. It also yields highly compressed interpretable representations.