Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Contrastive-center loss for deep neural networks   The deep convolutional neural network(CNN) has significantly raised the
performance of image classification and face recognition. Softmax is usually
used as supervision, but it only penalizes the classification loss. In this
paper, we propose a novel auxiliary supervision signal called contrastivecenter
loss, which can further enhance the discriminative power of the features, for
it learns a class center for each class. The proposed contrastive-center loss
simultaneously considers intra-class compactness and inter-class separability,
by penalizing the contrastive values between: (1)the distances of training
samples to their corresponding class centers, and (2)the sum of the distances
of training samples to their non-corresponding class centers. Experiments on
different datasets demonstrate the effectiveness of contrastive-center loss.

32
The categories of this paper are: atom-ph
15
The categories of this paper are: nucl-ex
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
31
The categories of this paper are: ao-sci
14
The categories of this paper are: math-ph
6
The categories of this paper are: astro-ph
4
The categories of this paper are: cond-mat
23
The categories of this paper are: solv-int
19
The categories of this paper are: eess
2
The categories of this paper are: math
22
The categories of this paper are: chao-dyn
7
The categories of this paper are: hep-th
11
The categories of this paper are: hep-lat
30
The categories of this paper are: q-alg