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Summary
The layer-wise structure of deep neural networks (DNNs) isolates the channel interactions in the same layer, which significantly impedes the efficient learning of DNNs. Several existing methods enable channel-wise information exchange via learning channel interdependence in a heuristic and empirical manner. Nevertheless, only informative channels are emphasized while other channels are suppressed in these approaches. This results in a low channel diversity , which impeds the generalization of DNNs. Our work aims to learn channel-wise interdependence and keep the channel diversity concurrently via designing optimal channel interaction patterns. We model the channel interaction pattern from a graph perspective, where the interactions can be regarded as information exchange on the channel graph . Based on this framework, we propose the Community Channel-Net (CC-Net), using a community-based graph topology for channel interaction. Each community contains channels with semantic commonalities, and the inter-community connections are activated among critical channels. With this structured and dynamic topology, the channels from the same community can learn channel interdependence , and those critical channels from distinct communities can gain more diverse features . CC-Net outperforms baselines on im-age classification tasks over various backbones with fewer computational costs.
