Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Class-conditioned Domain Generalization via Wasserstein Distributional
  Robust Optimization   Given multiple source domains, domain generalization aims at learning a
universal model that performs well on any unseen but related target domain. In
this work, we focus on the domain generalization scenario where domain shifts
occur among class-conditional distributions of different domains. Existing
approaches are not sufficiently robust when the variation of conditional
distributions given the same class is large. In this work, we extend the
concept of distributional robust optimization to solve the class-conditional
domain generalization problem. Our approach optimizes the worst-case
performance of a classifier over class-conditional distributions within a
Wasserstein ball centered around the barycenter of the source conditional
distributions. We also propose an iterative algorithm for learning the optimal
radius of the Wasserstein balls automatically. Experiments show that the
proposed framework has better performance on unseen target domain than
approaches without domain generalization.

31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
27
The categories of this paper are: comp-gas
16
The categories of this paper are: stat
8
The categories of this paper are: hep-ex
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
10
The categories of this paper are: q-bio
13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org
22
The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex
23
The categories of this paper are: solv-int
4
The categories of this paper are: cond-mat