Effective Sparsification of Neural Networks with Global Sparsity Constraint
arXiv:2105.01571
Abstract
Weight pruning is an effective technique to reduce the model size and inference time for deep neural networks in real-world deployments. However, since magnitudes and relative importance of weights are very different for different layers of a neural network, existing methods rely on either manual tuning or handcrafted heuristic rules to find appropriate pruning rates individually for each layer. This approach generally leads to suboptimal performance. In this paper, by directly working on the probability space, we propose an effective network sparsification method called {\it probabilistic masking} (ProbMask), which solves a natural sparsification formulation under global sparsity constraint. The key idea is to use probability as a global criterion for all layers to measure the weight importance. An appealing feature of ProbMask is that the amounts of weight redundancy can be learned automatically via our constraint and thus we avoid the problem of tuning pruning rates individually for different layers in a network. Extensive experimental results on CIFAR-10/100 and ImageNet demonstrate that our method is highly effective, and can outperform previous state-of-the-art methods by a significant margin, especially in the high pruning rate situation. Notably, the gap of Top-1 accuracy between our ProbMask and existing methods can be up to 10\%. As a by-product, we show ProbMask is also highly effective in identifying supermasks, which are subnetworks with high performance in a randomly weighted dense neural network.
References in corpus (21)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
- Categorical Reparameterization with Gumbel-Softmax
- Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation
- The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables
- Rethinking the Value of Network Pruning
- To prune, or not to prune: exploring the efficacy of pruning for model compression
- SNIP: Single-shot Network Pruning based on Connection Sensitivity
- Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Science
- The State of Sparsity in Deep Neural Networks
- Variational Dropout Sparsifies Deep Neural Networks
- SNAS: Stochastic Neural Architecture Search
- Sparse Networks from Scratch: Faster Training without Losing Performance
- Comparing Rewinding and Fine-tuning in Neural Network Pruning
- Deep Rewiring: Training very sparse deep networks
- Picking Winning Tickets Before Training by Preserving Gradient Flow
- Parameter Efficient Training of Deep Convolutional Neural Networks by Dynamic Sparse Reparameterization
- Soft Threshold Weight Reparameterization for Learnable Sparsity
- Discovering Neural Wirings
- Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection
- Operation-Aware Soft Channel Pruning using Differentiable Masks