# Respecting Domain Relations: Hypothesis Invariance for Domain Generalization

arXiv: https://arxiv.org/abs/2010.07591 · HTML: https://mlsanity.com/paper/2010.07591/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2020.

## Built on by (most cited first, 30 of 30)

- [Domain Generalization: A Survey](https://mlsanity.com/paper/2103.02503/) (2021)
- [Diffusion Models for Image Restoration and Enhancement: A Comprehensive Survey](https://mlsanity.com/paper/2308.09388/) (2023)
- [Domain Invariant Representation Learning with Domain Density Transformations](https://mlsanity.com/paper/2102.05082/) (2021)
- [StyLIP: Multi-Scale Style-Conditioned Prompt Learning for CLIP-based Domain Generalization](https://mlsanity.com/paper/2302.09251/) (2023)
- [Unbiased Faster R-CNN for Single-source Domain Generalized Object Detection](https://mlsanity.com/paper/2405.15225/) (2024)
- [A Survey on the Robustness of Computer Vision Models against Common Corruptions](https://mlsanity.com/paper/2305.06024/) (2023)
- [Unknown Prompt, the only Lacuna: Unveiling CLIP's Potential for Open Domain Generalization](https://mlsanity.com/paper/2404.00710/) (2024)
- [Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges](https://mlsanity.com/paper/2309.07438/) (2023)
- [Domain Invariant Masked Autoencoders for Self-supervised Learning from Multi-domains](https://mlsanity.com/paper/2205.04771/) (2022)
- [DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices](https://mlsanity.com/paper/2412.05823/) (2024)
- [Gradient-Guided Annealing for Domain Generalization](https://mlsanity.com/paper/2502.20162/) (2025)
- [Are demographically invariant models and representations in medical imaging fair?](https://mlsanity.com/paper/2305.01397/) (2023)
- [Conditional entropy minimization principle for learning domain invariant representation features](https://mlsanity.com/paper/2201.10460/) (2022)
- [Benchmarking Out-of-Distribution Generalization Capabilities of DNN-based Encoding Models for the Ventral Visual Cortex](https://mlsanity.com/paper/2406.16935/) (2024)
- [Transforming Future Data Center Operations and Management via Physical AI](https://mlsanity.com/paper/2504.04982/) (2025)
- [Learning to See Through a Baby's Eyes: Early Visual Diets Enable Robust Visual Intelligence in Humans and Machines](https://mlsanity.com/paper/2511.14440/) (2025)
- [Improving generalization by mimicking the human visual diet](https://mlsanity.com/paper/2206.07802/) (2022)
- [Joint covariate-alignment and concept-alignment: a framework for domain generalization](https://mlsanity.com/paper/2208.00898/) (2022)
- [Harmonizing and Merging Source Models for CLIP-based Domain Generalization](https://mlsanity.com/paper/2506.09446/) (2025)
- [Domain Generalization via Selective Consistency Regularization for Time Series Classification](https://mlsanity.com/paper/2206.07876/) (2022)
- [Trade-off between reconstruction loss and feature alignment for domain generalization](https://mlsanity.com/paper/2210.15000/) (2022)
- [EnfoMax: Domain Entropy and Mutual Information Maximization for Domain Generalized Face Anti-spoofing](https://mlsanity.com/paper/2302.08674/) (2023)
- [Parameter Exchange for Robust Dynamic Domain Generalization](https://mlsanity.com/paper/2311.13928/) (2023)
- [Towards Synchronous Memorizability and Generalizability with Site-Modulated Diffusion Replay for Cross-Site Continual Segmentation](https://mlsanity.com/paper/2406.18037/) (2024)
- [Attention Head Purification: A New Perspective to Harness CLIP for Domain Generalization](https://mlsanity.com/paper/2412.07226/) (2024)
- [Text-Driven Causal Representation Learning for Source-Free Domain Generalization](https://mlsanity.com/paper/2507.09961/) (2025)
- [Dual-supervised Asymmetric Co-training for Semi-supervised Medical Domain Generalization](https://mlsanity.com/paper/2509.20785/) (2025)
- [TADA: Temporal Adversarial Data Augmentation for Time Series Data](https://mlsanity.com/paper/2407.15174/) (2024)
- [NeuRN: Neuro-inspired Domain Generalization for Image Classification](https://mlsanity.com/paper/2505.06881/) (2025)
- [Unlocking Out-of-Distribution Generalization in Dynamics through Physics-Guided Augmentation](https://mlsanity.com/paper/2510.24216/) (2025)

## Abstract (arXiv)

In domain generalization, multiple labeled non-independent and non-identically distributed source domains are available during training while neither the data nor the labels of target domains are. Currently, learning so-called domain invariant representations (DIRs) is the prevalent approach to domain generalization. In this work, we define DIRs employed by existing works in probabilistic terms and show that by learning DIRs, overly strict requirements are imposed concerning the invariance. Particularly, DIRs aim to perfectly align representations of different domains, i.e. their input distributions. This is, however, not necessary for good generalization to a target domain and may even dispose of valuable classification information. We propose to learn so-called hypothesis invariant representations (HIRs), which relax the invariance assumptions by merely aligning posteriors, instead of aligning representations. We report experimental results on public domain generalization datasets to show that learning HIRs is more effective than learning DIRs. In fact, our approach can even compete with approaches using prior knowledge about domains.