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 1x1 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 1x1 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.

给定挑选或混合（Pick-or-Mix，PiX）模块，用于动态通道抽样，我们证明它可以有效地优化卷积神经网络（ConvNets）的性能，并在网络缩放和通道剪枝应用中取得最新的成果。