LAUSR

Convolutional neural networks (CNNs) have shown their great power in multiple computer vision tasks. However, many recent works improve their performance by adding more layers and parameters, which lead to computational redundancy in many application scenarios, making it harder to implement on low-end devices. To solve this problem, model pruning methods are proposed, which aim to lower the computational and memory requirements of CNNs. In this paper, we propose ScoringNet, a neural network (NN) based pruning criteria for structured pruning procedure. ScoringNet generates a set of scores for each output channel in a model, which is used to reconstruct a pruned model later in a structured pruning way. ScoringNet can also use the gradient information to generate better scores, making the pruned model perform better. By using NNs, there are fewer hyperparameters, making it easier to implement. Experiment results demonstrate that the proposed ScoringNet can outperform or achieve competitive results compared to many state-of-the-art methods in both postpruning and pruning-at-initialization setups.