PokerNet: Expanding Features Cheaply via Depthwise Convolutions

Pointwise convolution is usually utilized to expand or squeeze features in modern lightweight deep models. However, it takes up most of the overall computational cost (usually more than 90%). This paper proposes a novel Poker module to expand features by taking advantage of cheap depthwise convolution. As a result, the Poker module can greatly reduce the computational cost, and meanwhile generate a large number of effective features to guarantee the performance. The proposed module is standardized and can be employed wherever the feature expansion is needed. By varying the stride and the number of channels, different kinds of bottlenecks are designed to plug the proposed Poker module into the network. Thus, a lightweight model can be easily assembled. Experiments conducted on benchmarks reveal the effectiveness of our proposed Poker module. And our PokerNet models can reduce the computational cost by 7.1%−15.6%. PokerNet models achieve comparable or even higher recognition accuracy than previous state-of-the-art (SOTA) models on the ImageNet ILSVRC2012 classification dataset. Code is available at https://github.com/diaomin/pokernet.