  In the field of pattern classification, the training of deep learning
classifiers is mostly end-to-end learning, and the loss function is the
constraint on the final output (posterior probability) of the network, so the
existence of Softmax is essential. In the case of end-to-end learning, there is
usually no effective loss function that completely relies on the features of
the middle layer to restrict learning, resulting in the distribution of sample
latent features is not optimal, so there is still room for improvement in
classification accuracy. Based on the concept of Predefined Evenly-Distributed
Class Centroids (PEDCC), this article proposes a Softmax-free loss function
based on predefined optimal-distribution of latent features-POD Loss. The loss
function only restricts the latent features of the samples, including the
norm-adaptive Cosine distance between the latent feature vector of the sample
and the center of the predefined evenly-distributed class, and the correlation
between the latent features of the samples. Finally, Cosine distance is used
for classification. Compared with the commonly used Softmax Loss, some typical
Softmax related loss functions and PEDCC-Loss, experiments on several commonly
used datasets on several typical deep learning classification networks show
that the classification performance of POD Loss is always significant better
and easier to converge. Code is available in
https://github.com/TianYuZu/POD-Loss.
