Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: MiniMax Entropy Network: Learning Category-Invariant Features for Domain
  Adaptation   How to effectively learn from unlabeled data from the target domain is
crucial for domain adaptation, as it helps reduce the large performance gap due
to domain shift or distribution change. In this paper, we propose an
easy-to-implement method dubbed MiniMax Entropy Networks (MMEN) based on
adversarial learning. Unlike most existing approaches which employ a generator
to deal with domain difference, MMEN focuses on learning the categorical
information from unlabeled target samples with the help of labeled source
samples. Specifically, we set an unfair multi-class classifier named
categorical discriminator, which classifies source samples accurately but be
confused about the categories of target samples. The generator learns a common
subspace that aligns the unlabeled samples based on the target pseudo-labels.
For MMEN, we also provide theoretical explanations to show that the learning of
feature alignment reduces domain mismatch at the category level. Experimental
results on various benchmark datasets demonstrate the effectiveness of our
method over existing state-of-the-art baselines.

21
The categories of this paper are: adap-org
24
The categories of this paper are: dg-ga
27
The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
17
The categories of this paper are: q-fin
14
The categories of this paper are: math-ph
16
The categories of this paper are: stat
34
The categories of this paper are: chem-ph
9
The categories of this paper are: nlin
8
The categories of this paper are: hep-ex
5
The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph
30
The categories of this paper are: q-alg
22
The categories of this paper are: chao-dyn