GM-Net: Learning Features with More Efficiency

Understand

Deep Convolutional Neural Networks (CNNs) are capable of learning unprecedentedly effective features from images.

• Some researchers have struggled to enhance the parameters' efficiency using grouped convolution.
• However, the relation between the optimal number of convolutional groups and the recognition performance remains an open problem.
• In this paper, we propose a series of Basic Units (BUs) and a two-level merging strategy to construct deep CNNs, referred to as a joint Grouped Merging Net (GM-Net), which can produce joint grouped and reused deep features while maintaining the feature discriminability for classification tasks.