Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Certainty Driven Consistency Loss on Multi-Teacher Networks for
  Semi-Supervised Learning   One of the successful approaches in semi-supervised learning is based on the
consistency regularization. Typically, a student model is trained to be
consistent with teacher prediction for the inputs under different
perturbations. To be successful, the prediction targets given by teacher should
have good quality, otherwise the student can be misled by teacher.
Unfortunately, existing methods do not assess the quality of the teacher
targets. In this paper, we propose a novel Certainty-driven Consistency Loss
(CCL) that exploits the predictive uncertainty in the consistency loss to let
the student dynamically learn from reliable targets. Specifically, we propose
two approaches, i.e. Filtering CCL and Temperature CCL to either filter out
uncertain predictions or pay less attention on them in the consistency
regularization. We further introduce a novel decoupled framework to encourage
model difference. Experimental results on SVHN, CIFAR-10, and CIFAR-100
demonstrate the advantages of our method over a few existing methods.

15
The categories of this paper are: nucl-ex
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
13
The categories of this paper are: quant-ph
32
The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas
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The categories of this paper are: cmp-lg
30
The categories of this paper are: q-alg
2
The categories of this paper are: math
29
The categories of this paper are: funct-an
9
The categories of this paper are: nlin
14
The categories of this paper are: math-ph
22
The categories of this paper are: chao-dyn
10
The categories of this paper are: q-bio