Semantic clustering based deduction learning for image recognition and classification

Highlights：

• The paper proposes a high-level semantic mapping within semantic space to increase the semantic deduction ability of the deep neural network.

• Different from multi-level label learning, the proposed model can not only learn features but also deduct high-level semantic expression by itself.

• The deduction learning is realized by the semantic prior and the proposed random search for opposite semantic to ensure the smoothness of semantic clustering.

• The proposed model achieves stable convergence and high classification accuracy. It can be taken as a plug-in module for various deep learning applications.