Current Volume 10
Meta-Learning for Domain Generalization Under Distribution Shift: Methods, Benchmarks, and Open Challenges
Abstract
Domain generalization (DG) explores how a model trained on a fixed set of source domains can perform reliably on unseen tar-get domains. Meta-learning addresses this by training models to explic-itly learn to generalize, yet curated benchmarks have repeatedly shown that well-tuned evidence-based risk minimization (ERM) is a formidable baseline. This survey examines that tension with a focus on what ac-tually generalizes and why. We contribute three things. First, a bivari-ate taxonomy that cross references shift type (covariate, conditional, in-variant covariate, category, compound) against meta intervention level (data/augmentation, representation/gradient, optimization/parameter, prompt/foundation). Second, a structured comparison of benchmark fam-ilies: DomainBed, WILDS, single source, and open set settings that shows how benchmark choice, and not just method design, drives published conclusions. Third, a critical analysis of conditions under which meta-learning gains over ERM are real versus aphemeral, updated for the 2023-2026 period when foundation models, causal approaches, and meta prompting have substantially changed the landscape. We conclude with actionable open challenges and directions where meta-learning retains a genuine edge.
Keywords
Domain Generalization, Meta-Learning, Distribution Shift, Domainbed, WILDS, Benchmark Comparison, Transfer Learning
Citations
IRE Journals:
Aryanil Roy, Mainak Ghatak, Sananda Chatterjee, Kaushik Banerjee "Meta-Learning for Domain Generalization Under Distribution Shift: Methods, Benchmarks, and Open Challenges" Iconic Research And Engineering Journals Volume 9 Issue 12 2026 Page 3230-3238 https://doi.org/10.64388/IREV9I12-1719292
IEEE:
Aryanil Roy, Mainak Ghatak, Sananda Chatterjee, Kaushik Banerjee
"Meta-Learning for Domain Generalization Under Distribution Shift: Methods, Benchmarks, and Open Challenges" Iconic Research And Engineering Journals, 9(12) https://doi.org/10.64388/IREV9I12-1719292
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IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
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