Domain Generalisation via Risk Distribution Matching Article Swipe
Toan Nguyen
,
Kien Do
,
Bao Duong
,
Thin Nguyen
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2310.18598
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2310.18598
We propose a novel approach for domain generalisation (DG) leveraging risk distributions to characterise domains, thereby achieving domain invariance. In our findings, risk distributions effectively highlight differences between training domains and reveal their inherent complexities. In testing, we may observe similar, or potentially intensifying in magnitude, divergences between risk distributions. Hence, we propose a compelling proposition: Minimising the divergences between risk distributions across training domains leads to robust invariance for DG. The key rationale behind this concept is that a model, trained on domain-invariant or stable features, may consistently produce similar risk distributions across various domains. Building upon this idea, we propose Risk Distribution Matching (RDM). Using the maximum mean discrepancy (MMD) distance, RDM aims to minimise the variance of risk distributions across training domains. However, when the number of domains increases, the direct optimisation of variance leads to linear growth in MMD computations, resulting in inefficiency. Instead, we propose an approximation that requires only one MMD computation, by aligning just two distributions: that of the worst-case domain and the aggregated distribution from all domains. Notably, this method empirically outperforms optimising distributional variance while being computationally more efficient. Unlike conventional DG matching algorithms, RDM stands out for its enhanced efficacy by concentrating on scalar risk distributions, sidestepping the pitfalls of high-dimensional challenges seen in feature or gradient matching. Our extensive experiments on standard benchmark datasets demonstrate that RDM shows superior generalisation capability over state-of-the-art DG methods.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2310.18598
- https://arxiv.org/pdf/2310.18598
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388092667
All OpenAlex metadata
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https://openalex.org/W4388092667Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2310.18598Digital Object Identifier
- Title
-
Domain Generalisation via Risk Distribution MatchingWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-10-28Full publication date if available
- Authors
-
Toan Nguyen, Kien Do, Bao Duong, Thin NguyenList of authors in order
- Landing page
-
https://arxiv.org/abs/2310.18598Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2310.18598Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2310.18598Direct OA link when available
- Concepts
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Matching (statistics), Computer science, Variance (accounting), Computation, Benchmark (surveying), RDM, Scalar (mathematics), Mathematical optimization, Algorithm, Mathematics, Statistics, Geometry, Geodesy, Computer network, Accounting, Geography, BusinessTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.matching. | 217 |
| abstract_inverted_index.rationale | 73 |
| abstract_inverted_index.resulting | 144 |
| abstract_inverted_index.Minimising | 56 |
| abstract_inverted_index.aggregated | 170 |
| abstract_inverted_index.capability | 231 |
| abstract_inverted_index.challenges | 211 |
| abstract_inverted_index.compelling | 54 |
| abstract_inverted_index.efficient. | 187 |
| abstract_inverted_index.increases, | 131 |
| abstract_inverted_index.invariance | 68 |
| abstract_inverted_index.leveraging | 9 |
| abstract_inverted_index.magnitude, | 45 |
| abstract_inverted_index.optimising | 180 |
| abstract_inverted_index.worst-case | 166 |
| abstract_inverted_index.algorithms, | 192 |
| abstract_inverted_index.demonstrate | 225 |
| abstract_inverted_index.differences | 26 |
| abstract_inverted_index.discrepancy | 110 |
| abstract_inverted_index.divergences | 46, 58 |
| abstract_inverted_index.effectively | 24 |
| abstract_inverted_index.empirically | 178 |
| abstract_inverted_index.experiments | 220 |
| abstract_inverted_index.invariance. | 18 |
| abstract_inverted_index.outperforms | 179 |
| abstract_inverted_index.potentially | 42 |
| abstract_inverted_index.Distribution | 103 |
| abstract_inverted_index.characterise | 13 |
| abstract_inverted_index.computation, | 157 |
| abstract_inverted_index.consistently | 88 |
| abstract_inverted_index.conventional | 189 |
| abstract_inverted_index.distribution | 171 |
| abstract_inverted_index.intensifying | 43 |
| abstract_inverted_index.optimisation | 134 |
| abstract_inverted_index.proposition: | 55 |
| abstract_inverted_index.sidestepping | 206 |
| abstract_inverted_index.approximation | 151 |
| abstract_inverted_index.complexities. | 34 |
| abstract_inverted_index.computations, | 143 |
| abstract_inverted_index.concentrating | 201 |
| abstract_inverted_index.distributions | 11, 23, 61, 92, 121 |
| abstract_inverted_index.inefficiency. | 146 |
| abstract_inverted_index.distributional | 181 |
| abstract_inverted_index.distributions, | 205 |
| abstract_inverted_index.distributions. | 49 |
| abstract_inverted_index.distributions: | 162 |
| abstract_inverted_index.generalisation | 7, 230 |
| abstract_inverted_index.computationally | 185 |
| abstract_inverted_index.domain-invariant | 83 |
| abstract_inverted_index.high-dimensional | 210 |
| abstract_inverted_index.state-of-the-art | 233 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/17 |
| sustainable_development_goals[0].score | 0.41999998688697815 |
| sustainable_development_goals[0].display_name | Partnerships for the goals |
| citation_normalized_percentile |