Modern deep neural networks are highly overparameterized, and often of huge sizes. A number of post-training model compression techniques, such as distillation, pruning and quantization, can reduce the size of network parameters by a substantial fraction with little loss in performance. However, training a small network of the post-compression size de novo typically fails to reach the same level of accuracy achieved by compression of a large network, leading to a widely-held belief that gross overparameterization is essential to effective learning. In this work, we argue that this is not necessarily true. We describe a dynamic sparse reparameterization technique that closed the performance gap between a model compressed through iterative pruning and a model of the post-compression size trained de novo. We applied our method to training deep residual networks and showed that it outperformed existing reparameterization techniques, yielding the best accuracy for a given parameter budget for training. Compared to existing dynamic reparameterization methods that reallocate non-zero parameters during training, our approach achieved better performance at lower computational cost. Our method is not only of practical value for training under stringent memory constraints, but also potentially informative to theoretical understanding of generalization properties of overparameterized deep neural networks.
This paper presents a method for training neural networks where an efficient sparse/compressed representation is enforced throughout the training process, as opposed to starting with are large model and pruning down to a smaller size.  For this purpose a dynamic sparse reparameterization heuristic is proposed and validated using data from MNIST, CIFAR-10, and ImageNet.
My concerns with this work in its present form are two-fold.  First, from a novelty standpoint, the proposed pipeline can largely be viewed as introducing a couple heuristic modifications to the SET procedure from reference (Mocanu, et al., 2018), e.g., substituting an approximate threshold instead of sorting for removing weights, changing how new weights are redistributed, etc.  The considerable similarity was pointed out by anonymous commenters and I believe somewhat understated by the submission.  Regardless, even if practically effective, these changes seem more like reasonable engineering decisions to improve the speed/performance rather than research contributions that provide any real insights.  Moreover, there is no attendant analysis regarding convergence and/or stability of what is otherwise a sequence of iterates untethered to a specific energy function being minimized.
Of course all of this could potentially be overcome with a compelling series of experiments demonstrating the unequivocal utility of the proposed modifications.  But it is here that unfortunately the paper falls well short.  Despite its close kinship with SET, there are surprisingly no comparisons presented whatsoever.  Likewise only a single footnote mentions comparative results with DeepR (Bellec et al., 2017), which represents another related dynamic reparameterization method.  In a follow up response to anonymous public comments, some new tests using CIFAR-10 data are presented, but to me, proper evaluation requires full experimental details/settings and another round of review.
Moreover, the improvement over SET in these new results, e.g., from a 93.42 to 93.68 accuracy rate at 0.9 sparsity level, seems quite modest.  Note that the proposed pipeline has a wide range of tuning hyperparameters (occupying a nearly page-sized Table 3 in the Appendix), and depending on these settings relative to SET, one could easily envision this sort of minor difference evaporating completely.  But again, this is why I strongly believe that another round of review with detailed comparisons to SET and DeepR is needed.
Beyond this, the paper repeatedly mentions significant improvement over "start-of-the-art sparse compression methods." But this claim is completely unsupported, because all the tables and figures only report results from a single existing compression baseline, namely, the pruning method from (Zhu and Gupta, 2017) which is ultimately based on (Han et al., 2015).  But just in the last year alone there have been countless compression papers published in the top ML and CV conferences, and it is by no means established that the pruning heuristic from (Zhu and Gupta, 2017) is state-of-the-art.
Note also that reported results can be quite deceiving on the surface, because unless the network structure, data augmentation, and other experimental design details are exactly the same, specific numbers cannot be directly transferred across papers.  Additionally, numerous published results involve pruning at the activation level rather than specific weights.  This definitively sacrifices the overall compression rate/model size to achieve structured pruning that is more naturally advantageous to implementation in practical hardware (e.g., reducing FLOPs, run-time memory, etc.).  One quick example is Luo et al., "ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression," ICCV 2017, but there are many many others.
And as a final critique of the empirical section, why not report the full computational cost of training the proposed model relative to others?  For an engineered algorithmic proposal emphasizing training efficiency, this seems like an essential component.
In aggregate then, my feeling is that while the proposed pipeline may eventually prove to be practically useful, presently this paper does not contain a sufficient aggregation of novel research contribution and empirical validation.
Other comments:
- In Table 2, what is the baseline accuracy with no pruning?
- Can this method be easily extended to prune entire filters/activations?