We present a novel network pruning algorithm called Dynamic Sparse Training that can jointly find the optimal network parameters and sparse network structure in a unified optimization process with trainable pruning thresholds. These thresholds can have fine-grained layer-wise adjustments dynamically via backpropagation. We demonstrate that our dynamic sparse training algorithm can easily train very sparse neural network models with little performance loss using the same training epochs as dense models. Dynamic Sparse Training achieves prior art performance compared with other sparse training algorithms on various network architectures. Additionally, we have several surprising observations that provide strong evidence to the effectiveness and efficiency of our algorithm. These observations reveal the underlying problems of traditional three-stage pruning algorithms and present the potential guidance provided by our algorithm to the design of more compact network architectures.
## Update after the rebuttal
I appreciate the author's clarification in the rebuttal and the additional result on ImageNet, which addressed some of my concerns.
# Summary
This paper proposes a trainable mask layer in neural networks for compressing neural networks end-to-end. The main idea is to apply a differentiable mask to individual weights such that the mask itself is also trained through backpropagation. They also propose to add a regularization term that encourages weights are masked out as much as possible. The result on MNIST and CIFAR show that their method can achieve the highest (weight) compression rate and the lowest accuracy reduction compared to baselines. 
# Originality
- The idea of applying trainable mask to weights and regularizing toward masking out is quite interesting and new to my knowledge. 
# Quality
- The performance seems to be good, though it would be more convincing if the paper showed results on larger datasets like ImageNet. 
- The analysis is interesting, but I am not fully convinced by the "strong evidence to the efficiency and effectiveness of our algorithm". For example, the final layer's remaining ratio is constantly 1 in Figure 3, while it starts from nearly 0 in Figure 4. The paper also argues that the final layer was not that important in Figure 4 because the lower layers have not learned useful features. This seems not only contradictory to the result of Figure 3 but also inconsistent of the accuracy being quickly increasing up to near 90% while the remaining ratio is nearly 0 in Figure 4. 
- If the motivation of the sparse training is to reduce memory consumption AND computation, showing some results on the reduction of the computation cost after sparse training would important to complete the story. 
# Clarity
- The description of the main idea is not clear. 
- What are "structure gradient" and "performance gradient"? They are not mathematically defined in the paper.
- I do not understand how the proposed method can "recover" from pruned connection, although it seems to be indeed happening in the experiment. The paper claims that the use of long-tailed higher-order estimator H(x) makes it possible to recover. However, H(x) still seems to have flat lines where the derivative is 0. Is H(x) in Equation 3 and Figure 2d are showing "derivative" or step function itself? In any cases, I do not see how the gradient flows once a weight is masked out. 
# Significance
- This paper proposes an interesting idea (trainable mask), though I did not fully get how the mask is defined/trained and has a potential to recover after pruning. The analysis of the compression rate throughout training is interesting but does not seem to be fully convincing. It would be stronger if the paper 1) included more results on bigger datasets like ImageNet, 2) described the main idea more clearly, and 3) provided more convincing evidence why the proposed method is effective.