  This paper introduces the concept of uniform classification, which employs a
unified threshold to classify all samples rather than adaptive threshold
classifying each individual sample. We also propose the uniform classification
accuracy as a metric to measure the model's performance in uniform
classification. Furthermore, begin with a naive loss, we mathematically derive
a loss function suitable for the uniform classification, which is the BCE
function integrated with a unified bias. We demonstrate the unified threshold
could be learned via the bias. The extensive experiments on six classification
datasets and three feature extraction models show that, compared to the SoftMax
loss, the models trained with the BCE loss not only exhibit higher uniform
classification accuracy but also higher sample-wise classification accuracy. In
addition, the learned bias from BCE loss is very close to the unified threshold
used in the uniform classification. The features extracted by the models
trained with BCE loss not only possess uniformity but also demonstrate better
intra-class compactness and inter-class distinctiveness, yielding superior
performance on open-set tasks such as face recognition.
