Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: OTFace: Hard Samples Guided Optimal Transport Loss for Deep Face
  Representation   Face representation in the wild is extremely hard due to the large scale face
variations. To this end, some deep convolutional neural networks (CNNs) have
been developed to learn discriminative feature by designing properly
margin-based losses, which perform well on easy samples but fail on hard
samples. Based on this, some methods mainly adjust the weights of hard samples
in training stage to improve the feature discrimination. However, these methods
overlook the feature distribution property which may lead to better results
since the miss-classified hard samples may be corrected by using the
distribution metric. This paper proposes the hard samples guided optimal
transport (OT) loss for deep face representation, OTFace for short. OTFace aims
to enhance the performance of hard samples by introducing the feature
distribution discrepancy while maintain the performance on easy samples.
Specifically, we embrace triplet scheme to indicate hard sample groups in one
mini-batch during training. OT is then used to characterize the distribution
differences of features from the high level convolutional layer. Finally, we
integrate the margin-based-softmax (e.g. ArcFace or AM-Softmax) and OT to guide
deep CNN learning. Extensive experiments are conducted on several benchmark
databases. The quantitative results demonstrate the advantages of the proposed
OTFace over state-of-the-art methods.

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The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
2
The categories of this paper are: math
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-lat
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The categories of this paper are: funct-an
9
The categories of this paper are: nlin
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The categories of this paper are: nucl-ex
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The categories of this paper are: quant-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: math-ph
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The categories of this paper are: stat
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The categories of this paper are: adap-org
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The categories of this paper are: supr-con
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The categories of this paper are: mtrl-th