Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: More Information Supervised Probabilistic Deep Face Embedding Learning   Researches using margin based comparison loss demonstrate the effectiveness
of penalizing the distance between face feature and their corresponding class
centers. Despite their popularity and excellent performance, they do not
explicitly encourage the generic embedding learning for an open set recognition
problem. In this paper, we analyse margin based softmax loss in probability
view. With this perspective, we propose two general principles: 1) monotonic
decreasing and 2) margin probability penalty, for designing new margin loss
functions. Unlike methods optimized with single comparison metric, we provide a
new perspective to treat open set face recognition as a problem of information
transmission. And the generalization capability for face embedding is gained
with more clean information. An auto-encoder architecture called
Linear-Auto-TS-Encoder(LATSE) is proposed to corroborate this finding.
Extensive experiments on several benchmarks demonstrate that LATSE help face
embedding to gain more generalization capability and it boosted the single
model performance with open training dataset to more than $99\%$ on MegaFace
test.

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The categories of this paper are: plasm-ph
36
The categories of this paper are: cmp-lg
25
The categories of this paper are: patt-sol
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
19
The categories of this paper are: eess
2
The categories of this paper are: math
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int
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The categories of this paper are: hep-ex
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The categories of this paper are: nucl-th
10
The categories of this paper are: q-bio
37
The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph
13
The categories of this paper are: quant-ph
24
The categories of this paper are: dg-ga