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Group Softmax Loss With Discriminative Feature Grouping
In the supervised learning framework, a softmax cross-entropy loss is commonly applied to train deep neural networks for high-performance classification. It, however, demands large amount of annotated data and fails to learn the discriminative networks on a smaller amount of data. In this paper, we propose a novel loss measure to train the networks such that discriminative feature representation can be learned even on the smaller-scale dataset. By means of feature grouping, we effectively expose non-discriminative feature components to representation learning and formulate two types of group softmax losses to cope with the grouped features. The proposed method encourages discriminative representation across all feature components, and from a theoretical viewpoint it renders adversarial training which works for alleviating over-fitting especially on scarce training data. The experimental results on image classification tasks demonstrate that the proposed loss favorably improves performance of CNNs on various-scale data.