Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DART: Domain-Adversarial Residual-Transfer Networks for Unsupervised
  Cross-Domain Image Classification   The accuracy of deep learning (e.g., convolutional neural networks) for an
image classification task critically relies on the amount of labeled training
data. Aiming to solve an image classification task on a new domain that lacks
labeled data but gains access to cheaply available unlabeled data, unsupervised
domain adaptation is a promising technique to boost the performance without
incurring extra labeling cost, by assuming images from different domains share
some invariant characteristics. In this paper, we propose a new unsupervised
domain adaptation method named Domain-Adversarial Residual-Transfer (DART)
learning of Deep Neural Networks to tackle cross-domain image classification
tasks. In contrast to the existing unsupervised domain adaption approaches, the
proposed DART not only learns domain-invariant features via adversarial
training, but also achieves robust domain-adaptive classification via a
residual-transfer strategy, all in an end-to-end training framework. We
evaluate the performance of the proposed method for cross-domain image
classification tasks on several well-known benchmark data sets, in which our
method clearly outperforms the state-of-the-art approaches.

25
The categories of this paper are: patt-sol
24
The categories of this paper are: dg-ga
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
11
The categories of this paper are: hep-lat
3
The categories of this paper are: physics
35
The categories of this paper are: mtrl-th
32
The categories of this paper are: atom-ph
2
The categories of this paper are: math
19
The categories of this paper are: eess
20
The categories of this paper are: acc-phys
22
The categories of this paper are: chao-dyn