Generating Multi-Center Classifier via Conditional Gaussian Distribution
This work addresses the challenge of handling multiple local clusters within a single class in image classification, offering an incremental improvement over existing linear classifiers.
The paper tackles the problem of intra-class variability in image classification by proposing a multi-center classifier that models deep features with a Gaussian Mixture distribution, achieving competitive performance as a powerful alternative to linear classifiers without extra parameters or computational cost.
The linear classifier is widely used in various image classification tasks. It works by optimizing the distance between a sample and its corresponding class center. However, in real-world data, one class can contain several local clusters, e.g., birds of different poses. To address this complexity, we propose a novel multi-center classifier. Different from the vanilla linear 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, at test time we set the mean of the conditional Gaussian distribution as the class center of the linear classifier and follow the vanilla linear classifier outputs, thus requiring no additional parameters or computational overhead. Extensive experiments on image classification show that the proposed multi-center classifier is a powerful alternative to widely used linear classifiers. Code available at https://github.com/ZheminZhang1/MultiCenter-Classifier.