Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Adversarial Invariance   Data representations that contain all the information about target variables
but are invariant to nuisance factors benefit supervised learning algorithms by
preventing them from learning associations between these factors and the
targets, thus reducing overfitting. We present a novel unsupervised invariance
induction framework for neural networks that learns a split representation of
data through competitive training between the prediction task and a
reconstruction task coupled with disentanglement, without needing any labeled
information about nuisance factors or domain knowledge. We describe an
adversarial instantiation of this framework and provide analysis of its
working. Our unsupervised model outperforms state-of-the-art methods, which are
supervised, at inducing invariance to inherent nuisance factors, effectively
using synthetic data augmentation to learn invariance, and domain adaptation.
Our method can be applied to any prediction task, eg., binary/multi-class
classification or regression, without loss of generality.

22
The categories of this paper are: chao-dyn
14
The categories of this paper are: math-ph
8
The categories of this paper are: hep-ex
32
The categories of this paper are: atom-ph
35
The categories of this paper are: mtrl-th
6
The categories of this paper are: astro-ph
15
The categories of this paper are: nucl-ex
11
The categories of this paper are: hep-lat
21
The categories of this paper are: adap-org
24
The categories of this paper are: dg-ga
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
9
The categories of this paper are: nlin
34
The categories of this paper are: chem-ph
28
The categories of this paper are: alg-geom
18
The categories of this paper are: econ