  The loss function is a key component in deep learning models. A commonly used
loss function for classification is the cross entropy loss, which is a simple
yet effective application of information theory for classification problems.
Based on this loss, many other loss functions have been proposed,~\emph{e.g.},
by adding intra-class and inter-class constraints to enhance the discriminative
ability of the learned features. However, these loss functions fail to consider
the connections between the feature distribution and the model structure.
Aiming at addressing this problem, we propose a channel correlation loss
(CC-Loss) that is able to constrain the specific relations between classes and
channels as well as maintain the intra-class and the inter-class separability.
CC-Loss uses a channel attention module to generate channel attention of
features for each sample in the training stage. Next, an Euclidean distance
matrix is calculated to make the channel attention vectors associated with the
same class become identical and to increase the difference between different
classes. Finally, we obtain a feature embedding with good intra-class
compactness and inter-class separability.Experimental results show that two
different backbone models trained with the proposed CC-Loss outperform the
state-of-the-art loss functions on three image classification datasets.
