Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Barycentric-alignment and invertibility for domain generalization   We revisit Domain Generalization (DG) problem, where the hypotheses are
composed of a common representation mapping followed by a labeling function.
Popular DG methods optimize a well-known upper bound to the risk in the unseen
domain. However, the bound contains a term that is not optimized due to its
dual dependence on the representation mapping and the unknown optimal labeling
function for the unseen domain. We derive a new upper bound free of terms
having such dual dependence by imposing mild assumptions on the loss function
and an invertibility requirement on the representation map when restricted to
the low-dimensional data manifold. The derivation leverages old and recent
transport inequalities that link optimal transport metrics with
information-theoretic measures. Our bound motivates a new algorithm for DG
comprising Wasserstein-2 barycenter cost for feature alignment and mutual
information or autoencoders for enforcing approximate invertibility.
Experiments on several datasets demonstrate superior performance compared to
the state-of-the-art DG algorithms.

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The categories of this paper are: math-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: hep-lat
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The categories of this paper are: cmp-lg
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The categories of this paper are: atom-ph
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The categories of this paper are: physics
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The categories of this paper are: eess
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The categories of this paper are: nucl-ex
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The categories of this paper are: chem-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: dg-ga
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The categories of this paper are: nlin
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The categories of this paper are: q-alg
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The categories of this paper are: patt-sol
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-ph