Back to Simplicity: How to Train Accurate BNNs from Scratch?

19 Jun 2019Joseph BethgeHaojin YangMarvin BornsteinChristoph Meinel

Binary Neural Networks (BNNs) show promising progress in reducing computational and memory costs but suffer from substantial accuracy degradation compared to their real-valued counterparts on large-scale datasets, e.g., ImageNet. Previous work mainly focused on reducing quantization errors of weights and activations, whereby a series of approximation methods and sophisticated training tricks have been proposed... (read more)

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