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We present Random Partition Relaxation (RPR), a method for strongquantization of neural networks weight to binary (+1/-1) and ternary (+1/0/-1)values. Starting from a pre-trained model, we quantize the weights and thenrelax random partitions of them to their continuous values for retrainingbefore re-quantizing them and switching to another weight partition for furtheradaptation. We demonstrate binary and ternary-weight networks with accuraciesbeyond the state-of-the-art for GoogLeNet and competitive performance forResNet-18 and ResNet-50 using an SGD-based training method that can easily beintegrated into existing frameworks.

RPR: Random Partition Relaxation for Training; Binary and Ternary Weight Neural Networks