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
this https URL

基于深度特征的高斯混合分布，我们提出了一种新颖的多中心分类器，能够更有效地捕捉图像分类中的类内局部结构，而且不需要额外的参数或计算开销。