  Most classification models can be considered as the process of matching
templates. However, when intra-class uncertainty/variability is not considered,
especially for datasets containing unbalanced classes, this may lead to
classification errors. To address this issue, we propose a loss function with
intra-class uncertainty following Gaussian distribution. Specifically, in our
framework, the features extracted by deep networks of each class are
characterized by independent Gaussian distribution. The parameters of
distribution are learned with a likelihood regularization along with other
network parameters. The means of the Gaussian play a similar role as the center
anchor in existing methods, and the variance describes the uncertainty of
different classes. In addition, similar to the inter-class margin in
traditional loss functions, we introduce a margin to intra-class uncertainty to
make each cluster more compact and reduce the imbalance of feature distribution
from different categories. Based on MNIST, CIFAR, ImageNet, and Long-tailed
CIFAR analyses, the proposed approach shows improved classification
performance, through learning a better class representation.
