Learned Threshold Pruning

This paper presents a novel differentiable method for unstructured weight pruning of deep neural networks. Our learned-threshold pruning (LTP) method learns per-layer thresholds via gradient descent, unlike conventional methods where they are set as input. Making thresholds trainable also makes LTP computationally efficient, hence scalable to deeper networks. For example, it takes 30 epochs for LTP to prune ResNet50 on ImageNet by a factor of 9.1. This is in contrast to other methods that search for per-layer thresholds via a computationally intensive iterative pruning and fine-tuning process. Additionally, with a novel differentiable L0 regularization, LTP is able to operate effectively on architectures with batch-normalization. This is important since L1 and L2 penalties lose their regularizing effect in networks with batch-normalization. Finally, LTP generates a trail of progressively sparser networks from which the desired pruned network can be picked based on sparsity and performance requirements. These features allow LTP to achieve competitive compression rates on ImageNet networks such as AlexNet (26.4× compression with 79.1% Top-5 accuracy) and ResNet50 (9.1× compression with 92.0% Top-5 accuracy). We also show that LTP effectively prunes modern textitcompact architectures, such as EfficientNet, MobileNetV2 and MixNet.