Generating Multi-Center Classifier via Conditional Gaussian Distribution
Zhemin Zhang Xun Gong
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.