Modern Convolutional Neural Networks (CNNs) are complex, encompassing millions of parameters. Their deployment exerts computational, storage and energy demands, particularly on embedded platforms. Existing approaches to prune or sparsify CNNs require retraining to maintain inference accuracy. Such retraining is not feasible in some contexts. In this paper, we explore the sparsification of CNNs by proposing three model-independent methods. Our methods are applied on-the-fly and require no retraining. We show that the state-of-the-art models' weights can be reduced by up to 73 (compression factor of 3.7x) without incurring more than 5 loss in Top-5 accuracy. Additional fine-tuning gains only 8 in sparsity, which indicates that our fast on-the-fly methods are effective.
@cite_9 proposed ConvNets as a framework that can be used to iteratively learn sparsified neural connections through correlations among neural activations. The connections are dropped iteratively one layer at a time and the model is retrained.
@cite_9
2189774688
This paper proposes to learn high-performance deep ConvNets with sparse neural connections, referred to as sparse ConvNets, for face recognition. The sparse ConvNets are learned in an iterative way, each time one additional layer is sparsified and the entire model is re-trained given the initial weights learned in previous iterations. One important finding is that directly training the sparse ConvNet from scratch failed to find good solutions for face recognition, while using a previously learned denser model to properly initialize a sparser model is critical to continue learning effective features for face recognition. This paper also proposes a new neural correlation-based weight selection criterion and empirically verifies its effectiveness in selecting informative connections from previously learned models in each iteration. When taking a moderately sparse structure (26 -76 of weights in the dense model), the proposed sparse ConvNet model significantly improves the face recognition performance of the previous state-of-theart DeepID2+ models given the same training data, while it keeps the performance of the baseline model with only 12 of the original parameters.