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Binarization is a powerful compression technique for neural networks,significantly reducing FLOPs, but often results in a significant drop in modelperformance. To address this issue, partial binarization techniques have beendeveloped, but a systematic approach to mixing binary and full-precisionparameters in a single network is still lacking. In this paper, we propose acontrolled approach to partial binarization, creating a budgeted binary neuralnetwork (B2NN) with our MixBin strategy. This method optimizes the mixing ofbinary and full-precision components, allowing for explicit selection of thefraction of the network to remain binary. Our experiments show that B2NNscreated using MixBin outperform those from random or iterative searches andstate-of-the-art layer selection methods by up to 3% on the ImageNet-1Kdataset. We also show that B2NNs outperform the structured pruning baseline byapproximately 23% at the extreme FLOP budget of 15%, and perform well in objecttracking, with up to a 12.4% relative improvement over other baselines.Additionally, we demonstrate that B2NNs developed by MixBin can be transferredacross datasets, with some cases showing improved performance over directlyapplying MixBin on the downstream data.

Partial Binarization of Neural Networks for Budget-Aware Efficient Learning