Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Boosting Unsupervised Domain Adaptation with Soft Pseudo-label and
  Curriculum Learning   By leveraging data from a fully labeled source domain, unsupervised domain
adaptation (UDA) improves classification performance on an unlabeled target
domain through explicit discrepancy minimization of data distribution or
adversarial learning. As an enhancement, category alignment is involved during
adaptation to reinforce target feature discrimination by utilizing model
prediction. However, there remain unexplored problems about pseudo-label
inaccuracy incurred by wrong category predictions on target domain, and
distribution deviation caused by overfitting on source domain. In this paper,
we propose a model-agnostic two-stage learning framework, which greatly reduces
flawed model predictions using soft pseudo-label strategy and avoids
overfitting on source domain with a curriculum learning strategy.
Theoretically, it successfully decreases the combined risk in the upper bound
of expected error on the target domain. At the first stage, we train a model
with distribution alignment-based UDA method to obtain soft semantic label on
target domain with rather high confidence. To avoid overfitting on source
domain, at the second stage, we propose a curriculum learning strategy to
adaptively control the weighting between losses from the two domains so that
the focus of the training stage is gradually shifted from source distribution
to target distribution with prediction confidence boosted on the target domain.
Extensive experiments on two well-known benchmark datasets validate the
universal effectiveness of our proposed framework on promoting the performance
of the top-ranked UDA algorithms and demonstrate its consistent superior
performance.

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The categories of this paper are: cmp-lg
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The categories of this paper are: patt-sol
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The categories of this paper are: solv-int
3
The categories of this paper are: physics
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
20
The categories of this paper are: acc-phys
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The categories of this paper are: eess
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The categories of this paper are: supr-con
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The categories of this paper are: ao-sci
16
The categories of this paper are: stat
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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: bayes-an
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The categories of this paper are: hep-ex
2
The categories of this paper are: math