Pick-or-Mix: Dynamic Channel Sampling for ConvNets

Ashish Kumar, Daneul Kim, Jaesik Park, Laxmidhar Behera; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 5873-5882

Channel pruning approaches for convolutional neural networks (ConvNets) deactivate the channels statically or dynamically and require special implementation. In addition channel squeezing in representative ConvNets is carried out via 1 x 1 convolutions which dominates a large portion of computations and network parameters. Given these challenges we propose an effective multi-purpose module for dynamic channel sampling namely Pick-or-Mix (PiX) which does not require special implementation. PiX divides a set of channels into subsets and then picks from them where the picking decision is dynamically made per each pixel based on the input activations. We plug PiX into prominent ConvNet architectures and verify its multi-purpose utilities. After replacing 1 x 1 channel squeezing layers in ResNet with PiX the network becomes 25% faster without losing accuracy. We show that PiX allows ConvNets to learn better data representation than widely adopted approaches to enhance networks' representation power (e.g. SE CBAM AFF SKNet and DWP). We also show that PiX achieves state-of-the-art performance on network downscaling and dynamic channel pruning applications.