Probabilistic Binary Neural Networks

Jorn W.T. Peters, Tim Genewein, Max Welling

Abstract: Low bit-width weights and activations are an effective way of combating the increasing need for both memory and compute power of Deep Neural Networks. In this work, we present a probabilistic training method for Neural Network with both binary weights and activations, called PBNet. By embracing stochasticity during training, we circumvent the need to approximate the gradient of functions for which the derivative is zero almost always, such as $\textrm{sign}(\cdot)$, while still obtaining a fully Binary Neural Network at test time. Moreover, it allows for anytime ensemble predictions for improved performance and uncertainty estimates by sampling from the weight distribution. Since all operations in a layer of the PBNet operate on random variables, we introduce stochastic versions of Batch Normalization and max pooling, which transfer well to a deterministic network at test time. We evaluate two related training methods for the PBNet: one in which activation distributions are propagated throughout the network, and one in which binary activations are sampled in each layer. Our experiments indicate that sampling the binary activations is an important element for stochastic training of binary Neural Networks.

Keywords: binary neural Network, efficient deep learning, stochastic training, discrete neural network, efficient inference

TL;DR: We introduce a stochastic training method for training Binary Neural Network with both binary weights and activations.

Data: [CIFAR-10](https://paperswithcode.com/dataset/cifar-10)