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BinaryConnect (BC) and its many variations have become the de facto standardfor neural network quantization. However, our understanding of the innerworkings of BC is still quite limited. We attempt to close this gap in fourdifferent aspects: (a) we show that existing quantization algorithms, includingpost-training quantization, are surprisingly similar to each other; (b) weargue for proximal maps as a natural family of quantizers that is both easy todesign and analyze; (c) we refine the observation that BC is a special case ofdual averaging, which itself is a special case of the generalized conditionalgradient algorithm; (d) consequently, we propose ProxConnect (PC) as ageneralization of BC and we prove its convergence properties by exploiting theestablished connections. We conduct experiments on CIFAR-10 and ImageNet, andverify that PC achieves competitive performance.

Demystifying and Generalizing BinaryConnect