Paper Info
Title
Fire Together Wire Together: A Dynamic Pruning Approach with Self-Supervised Mask Prediction
Abstract
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 hyper- parameter selection to realize a computational budget. Our contribution is two-fold: 1) decoupled task and pruning losses. 2) Simple hyperparameter selection that enables FLOPs reduction estimation before training. Inspired by the Hebbian theory in Neuroscience: “neurons that fire together wire together”, 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 for 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 lower drop in accuracy with up to 13% improvement in FLOPs reduction.
Year
DOI
Venue
2022
10.1109/CVPR52688.2022.01213
IEEE Conference on Computer Vision and Pattern Recognition
Keywords
DocType
Volume
Efficient learning and inferences, Deep learning architectures and techniques
Conference
2022
Issue
Citations 
PageRank 
1
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Sara Elkerdawy101.35
Mostafa Elhoushi201.35
Hong Zhang358274.33
Ray Nilanjan454155.39