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

• URL: http://arxiv.org/abs/2203.12928v1
• Date: Thu, 24 Mar 2022 08:21:28 GMT
• Title: The Fixed Sub-Center: A Better Way to Capture Data Complexity
• Authors: Zhemin Zhang, Xun Gong
• Abstract summary: We propose to use Fixed Sub-Center (F-SC) to create more discrepant sub-centers. The experimental results show that F-SC significantly improves the accuracy of both image classification and fine-grained recognition tasks.
• Score: 1.583842747998493
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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