Reviews: Towards Accurate Binary Convolutional Neural Network

The paper describes a new method for training binary convolutional neural networks. The key insight here is to approximate the real valued weights via a linear combination of M binary basis weights. The coefficients for reconstructing the real weights can be found using least squares in the forward pass, and then pulled outside the convolution to allow for fast binary convolution at test time. A similar approach is taken for the activations, but in this case the weights and shifts are trained as normal during backpropagation. The result is a network that requires M more binary convolutions than a straightforward binary neural network, but it is expected that these will be significantly more hardware friendly.