Generating Multi-Center Classifier via Conditional Gaussian Distribution

In real-world data, one class can contain several local clusters, e.g., birds of different poses, which makes it difficult to represent the feature distribution of each class using only a single center. Existing intra-class multimodal representation methods employ sub-centers to capture intra-class variations. However, these sub-center methods have some limitations, i.e., they ignore the relationship between sub-centers and do not ensure the diversity of sub-centers. To address these limitations, we propose a novel multi-center classifier. Different from the vanilla multi-center classifier, our proposal is established on the assumption that the deep features of the training set follow a Gaussian Mixture distribution. Specifically, we create a conditional Gaussian distribution for each class and then sample multiple sub-centers from that distribution to extend the linear classifier. This approach allows the model to capture intra-class local structures more efficiently. In addition, we propose a novel label assignment strategy, the Multi-Center Class Label, to ensure that each sub-center is effectively involved in the training. Extensive experiments on various recognition benchmarks like ImageNet, CIFAR, and Mini-ImageNet demonstrate the effectiveness of our proposal.