The Fixed Sub-Center: A Better Way to Capture Data Complexity

Zhemin Zhang; Xun Gong

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arXiv · 2203.12928

The Fixed Sub-Center: A Better Way to Capture Data Complexity

Zhemin Zhang·Xun Gong

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Abstract

Treating class with a single center may hardly capture data distribution complexities. Using multiple sub-centers is an alternative way to address this problem. However, highly correlated sub-classes, the classifier's parameters grow linearly with the number of classes, and lack of intra-class compactness are three typical issues that need to be addressed in existing multi-subclass methods. To this end, we propose to use Fixed Sub-Center (F-SC), which allows the model to create more discrepant sub-centers while saving memory and cutting computational costs considerably. The F-SC specifically, first samples a class center Ui for each class from a uniform distribution, and then generates a normal distribution for each class, where the mean is equal to Ui. Finally, the sub-centers are sampled based on the normal distribution corresponding to each class, and the sub-centers are fixed during the training process avoiding the overhead of gradient calculation. Moreover, F-SC penalizes the Euclidean distance between the samples and their corresponding sub-centers, it helps remain intra-compactness. The experimental results show that F-SC significantly improves the accuracy of both image classification and fine-grained recognition tasks.

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Zhemin Zhang, Xun Gong. 2022-03-24. The Fixed Sub-Center: A Better Way to Capture Data Complexity. https://arxiv.org/abs/2203.12928

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