Dynamic model pruning is a recent direction that allows for the inference of a different sub-network for each input sample during deployment. However, current dynamic methods rely on learning a continuous channel gating through regularization by inducing sparsity loss. This formulation introduces complexity in balancing different losses (e.g task loss, regularization loss). In addition, regularization-based methods lack transparent tradeoff hyperparameter selection to realize computational budget. Our contribution is twofold: 1) decoupled task and pruning training. 2) Simple hyperparameter selection that enables FLOPs reduction estimation before training. We propose to predict a mask to process k filters in a layer based on the activation of its previous layer. We pose the problem as a self-supervised binary classification problem. Each mask predictor module is trained to predict if the log-likelihood of each filter in the current layer belongs to the top-k activated filters. The value k is dynamically estimated for each input based on a novel criterion using the mass of heatmaps. We show experiments on several neural architectures, such as VGG, ResNet, and MobileNet on CIFAR and ImageNet datasets. On CIFAR, we reach similar accuracy to SOTA methods with 15% and 24% higher FLOPs reduction. Similarly in ImageNet, we achieve a lower drop in accuracy with up to 13% improvement in FLOPs reduction.

本研究提出了一种基于 Hebbian 理论的动态网络剪枝方法，通过训练一个自我监督的二分类模型来预测每个层中需要处理的卷积核，从而实现 FLOPs 的减少，同时避免通过规范化引入复杂度来平衡各种损失，并实现透明的超参数选择。该方法在 CIFAR 和 ImageNet 数据集上的实验结果表明，与 SOTA 方法相比，具有相似的准确度和更高的 FLOPs 减少率。

共同激活 共同连接：一种带自监督掩码预测的动态剪枝方法

共同激活共同连接：一种带自监督掩码预测的动态剪枝方法