mlsanityData to 7 Oct 2026

HYPO: Hyperspherical Out-of-Distribution Generalization

arXiv 2402.07785 ↗First posted 12 Feb 2024cs.LGcited by 13

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Abstract (arXiv)

Out-of-distribution (OOD) generalization is critical for machine learning models deployed in the real world. However, achieving this can be fundamentally challenging, as it requires the ability to learn invariant features across different domains or environments. In this paper, we propose a novel framework HYPO (HYPerspherical OOD generalization) that provably learns domain-invariant representations in a hyperspherical space. In particular, our hyperspherical learning algorithm is guided by intra-class variation and inter-class separation principles -- ensuring that features from the same class (across different training domains) are closely aligned with their class prototypes, while different class prototypes are maximally separated. We further provide theoretical justifications on how our prototypical learning objective improves the OOD generalization bound. Through extensive experiments on challenging OOD benchmarks, we demonstrate that our approach outperforms competitive baselines and achieves superior performance. Code is available at https://github.com/deeplearning-wisc/hypo.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 58 of its 79 references that are in the map. First posted on arXiv 12 Feb 2024.

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58 of 79 references.

References · 58

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38 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 7; 2025: 4; 2026: 2.

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  1. 2025Controlling Neural Collapse Enhances Out-of-Distribution Detection and Transfer Learningcited by 11
  2. 2025Raising the Bar in Graph OOD Generalization: Invariant Learning Beyond Explicit Environment Modelingcited by 9
  3. 2024Temporal Test-Time Adaptation with State-Space Modelscited by 6
  4. 2024Out-of-Distribution Learning with Human Feedbackcited by 6
  5. 2024Hyperbolic Metric Learning for Visual Outlier Detectioncited by 3
  6. 2024Bridging OOD Detection and Generalization: A Graph-Theoretic Viewcited by 3
  7. 2026T-QPM: Enabling Temporal Out-Of-Distribution Detection and Domain Generalization for Vision-Language Models in Open-Worldcited by 3
  8. 2024Out-of-Distribution Detection with Attention Head Masking for Multimodal Document Classificationcited by 2
  9. 2025SphOR: A Representation Learning Perspective on Open-set Recognition for Identifying Unknown Classes in Deep Learning Modelscited by 1
  10. 2024Resilience and Security of Deep Neural Networks Against Intentional and Unintentional Perturbations: Survey and Research Challengescited by 0
  11. 2024Maximally Separated Active Learningcited by 0
  12. 2025Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insightscited by 0
1 more
  1. 2026GLASS: Graph-Language Alignment with Spherical Scoring for Transferable Graph-Level Anomaly Detectioncited by 0