Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Confidence Regularized Self-Training   Recent advances in domain adaptation show that deep self-training presents a
powerful means for unsupervised domain adaptation. These methods often involve
an iterative process of predicting on target domain and then taking the
confident predictions as pseudo-labels for retraining. However, since
pseudo-labels can be noisy, self-training can put overconfident label belief on
wrong classes, leading to deviated solutions with propagated errors. To address
the problem, we propose a confidence regularized self-training (CRST)
framework, formulated as regularized self-training. Our method treats
pseudo-labels as continuous latent variables jointly optimized via alternating
optimization. We propose two types of confidence regularization: label
regularization (LR) and model regularization (MR). CRST-LR generates soft
pseudo-labels while CRST-MR encourages the smoothness on network output.
Extensive experiments on image classification and semantic segmentation show
that CRSTs outperform their non-regularized counterpart with state-of-the-art
performance. The code and models of this work are available at
https://github.com/yzou2/CRST.

31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph
7
The categories of this paper are: hep-th
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The categories of this paper are: hep-ex
2
The categories of this paper are: math
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The categories of this paper are: stat
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The categories of this paper are: q-fin
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The categories of this paper are: funct-an
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The categories of this paper are: nlin
0
The categories of this paper are: hep-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: nucl-ex
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The categories of this paper are: gr-qc
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The categories of this paper are: supr-con
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The categories of this paper are: dg-ga