论文标题
量化感知训练中的渠道修剪：一种自适应投影梯度下降切解方法
Channel Pruning In Quantization-aware Training: An Adaptive Projection-gradient Descent-shrinkage-splitting Method
论文作者
论文摘要
我们提出了一种自适应投影梯度下降切解方法（APGDSSSM），以将基于惩罚的渠道修剪整合到量化感知训练（QAT）中。 APGDSSM同时搜索量化子空间和稀疏子空间中的权重。 APGDSSM使用收缩操作员和一种分裂技术来产生稀疏的重量，以及组的套索惩罚，以将重量稀疏推向通道稀疏。此外，我们提出了一种新颖的互补转换的L1惩罚，以稳定训练以进行极端压缩。
We propose an adaptive projection-gradient descent-shrinkage-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 l1 penalty to stabilize the training for extreme compression.
