Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient Domain Generalization via Common-Specific Low-Rank
  Decomposition   Domain generalization refers to the task of training a model which
generalizes to new domains that are not seen during training. We present CSD
(Common Specific Decomposition), for this setting,which jointly learns a common
component (which generalizes to new domains) and a domain specific component
(which overfits on training domains). The domain specific components are
discarded after training and only the common component is retained. The
algorithm is extremely simple and involves only modifying the final linear
classification layer of any given neural network architecture. We present a
principled analysis to understand existing approaches, provide identifiability
results of CSD,and study effect of low-rank on domain generalization. We show
that CSD either matches or beats state of the art approaches for domain
generalization based on domain erasure, domain perturbed data augmentation, and
meta-learning. Further diagnostics on rotated MNIST, where domains are
interpretable, confirm the hypothesis that CSD successfully disentangles common
and domain specific components and hence leads to better domain generalization.

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The categories of this paper are: hep-ex
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The categories of this paper are: ao-sci
3
The categories of this paper are: physics
22
The categories of this paper are: chao-dyn
30
The categories of this paper are: q-alg
26
The categories of this paper are: bayes-an
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The categories of this paper are: alg-geom
15
The categories of this paper are: nucl-ex
18
The categories of this paper are: econ
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The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
19
The categories of this paper are: eess
32
The categories of this paper are: atom-ph
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The categories of this paper are: math
29
The categories of this paper are: funct-an
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The categories of this paper are: plasm-ph