Abstract
In feature representation learning, robust features are expected to have intra-class compactness and inter-class separability. The traditional softmax loss concept ignores the intra-class compactness. Hence the discriminative power of deep features is weakened. This paper proposes a constrained center loss (CCL) to enable CNNs to extract robust features. Unlike the general center loss (CL) concept, class centers are analytically updated from the deep features in our formulation. In addition, we propose to use the entire training set to approximate class centers. By doing so, class centers can better capture the global information of feature space. To improve training efficiency, an alternative algorithm is proposed to optimize the joint supervision of softmax loss and CCL. Experiments are performed on four benchmark datasets. The results demonstrate that the proposed scheme outperforms several existing architectures.
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Acknowledgements
The work presented in this paper is supported by a research grant from City University of Hong Kong (7005223).
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Shi, Z., Wang, H., Leung, CS., Sum, J. (2020). Constrained Center Loss for Image Classification. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1333. Springer, Cham. https://doi.org/10.1007/978-3-030-63823-8_9
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DOI: https://doi.org/10.1007/978-3-030-63823-8_9
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