The Implicit and Explicit Regularization Effects of Dropout

Jul 12, 2020

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Dropout is a widely-used regularization technique, often required to obtain state-of-the-art for a number of architectures. This work observes that dropout introduces two distinct but entangled regularization effects: an explicit effect which occurs since dropout modifies the expected training objective, and an implicit effect from stochasticity in the dropout gradients. We disentangle these two effects, deriving analytic simplifications which characterize each effect in terms of the derivatives of the model and loss. Our simplified regularizers accurately capture the important aspects of dropout: we demonstrate that they can faithfully replace dropout in practice.

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About ICML 2020

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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