Channel Pruning in Quantization-aware Training: an Adaptive Projection-gradient Descent-shrinkage-splitting Method

We propose an adaptive projection-gradient descentshrinkage- splitting method (APGDSSM) to integrate penalty based channel pruning into quantization-aware training (QAT). APGDSSM concurrently searches weights in both the quantized subspace and the sparse subspace. APGDSSM uses shrinkage operator and a splitting technique to create sparse weights, as well as the Group Lasso penalty to push the weight sparsity into channel sparsity. In addition, we propose a novel complementary transformed l
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penalty to stabilize the training for extreme compression.