Reintroducing Straight-Through Estimators as Principled Methods for Stochastic Binary Networks
This work addresses the optimization problem for binary neural networks, offering a theoretical foundation for practitioners, though it is incremental as it builds on known empirical approaches.
The paper tackles the challenge of training neural networks with binary weights and activations by providing a principled derivation of straight-through estimators from stochastic binary network models, resulting in a systematic analysis that explains existing empirical methods and their limitations.
Training neural networks with binary weights and activations is a challenging problem due to the lack of gradients and difficulty of optimization over discrete weights. Many successful experimental results have been achieved with empirical straight-through (ST) approaches, proposing a variety of ad-hoc rules for propagating gradients through non-differentiable activations and updating discrete weights. At the same time, ST methods can be truly derived as estimators in the stochastic binary network (SBN) model with Bernoulli weights. We advance these derivations to a more complete and systematic study. We analyze properties, estimation accuracy, obtain different forms of correct ST estimators for activations and weights, explain existing empirical approaches and their shortcomings, explain how latent weights arise from the mirror descent method when optimizing over probabilities. This allows to reintroduce ST methods, long known empirically, as sound approximations, apply them with clarity and develop further improvements.