# GDA: Generalized Diffusion for Robust Test-time Adaptation

arXiv: https://arxiv.org/abs/2404.00095 · HTML: https://mlsanity.com/paper/2404.00095/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 50 references that are in the map.
- First posted on arXiv 29 Mar 2024.

## Builds on (most cited first, 30 of 38)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [Video Diffusion Models](https://mlsanity.com/paper/2204.03458/) (2022)
- [The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization](https://mlsanity.com/paper/2006.16241/) (2020)
- [Visual Prompt Tuning](https://mlsanity.com/paper/2203.12119/) (2022)
- [ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness](https://mlsanity.com/paper/1811.12231/) (2018)
- [Natural Adversarial Examples](https://mlsanity.com/paper/1907.07174/) (2019)
- [Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization](https://mlsanity.com/paper/1911.08731/) (2019)
- [Improved Techniques for Training Score-Based Generative Models](https://mlsanity.com/paper/2006.09011/) (2020)
- [Learning Robust Global Representations by Penalizing Local Predictive Power](https://mlsanity.com/paper/1905.13549/) (2019)
- [AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty](https://mlsanity.com/paper/1912.02781/) (2019)
- [Learning to Generalize: Meta-Learning for Domain Generalization](https://mlsanity.com/paper/1710.03463/) (2017)
- [Contrastive Learning for Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2007.15651/) (2020)
- [Domain Generalization with MixStyle](https://mlsanity.com/paper/2104.02008/) (2021)
- [Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty](https://mlsanity.com/paper/1906.12340/) (2019)
- [Diffusion Models for Adversarial Purification](https://mlsanity.com/paper/2205.07460/) (2022)
- [Domain Generalization via Model-Agnostic Learning of Semantic Features](https://mlsanity.com/paper/1910.13580/) (2019)
- [MEMO: Test Time Robustness via Adaptation and Augmentation](https://mlsanity.com/paper/2110.09506/) (2021)
- [Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming](https://mlsanity.com/paper/1907.07484/) (2019)
- [Deep Domain-Adversarial Image Generation for Domain Generalisation](https://mlsanity.com/paper/2003.06054/) (2020)
- [Semantic Image Synthesis via Diffusion Models](https://mlsanity.com/paper/2207.00050/) (2022)
- [Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources](https://mlsanity.com/paper/2007.08714/) (2020)
- [Zero-Shot Contrastive Loss for Text-Guided Diffusion Image Style Transfer](https://mlsanity.com/paper/2303.08622/) (2023)

## Built on by (most cited first, 14 of 14)

- [Test-Time Adaptation of 3D Point Clouds via Denoising Diffusion Models](https://mlsanity.com/paper/2411.14495/) (2024)
- [MOSAIC: Generating Consistent, Privacy-Preserving Scenes from Multiple Depth Views in Multi-Room Environments](https://mlsanity.com/paper/2503.13816/) (2025)
- [Point-Cache: Test-time Dynamic and Hierarchical Cache for Robust and Generalizable Point Cloud Analysis](https://mlsanity.com/paper/2503.12150/) (2025)
- [ETTA: Efficient Test-Time Adaptation for Vision-Language Models through Dynamic Embedding Updates](https://mlsanity.com/paper/2508.05898/) (2025)
- [Dance Across Shifts: Forward-Facilitation Continual Test-Time Adaptation through Dynamic Style Bridging](https://mlsanity.com/paper/2605.18608/) (2026)
- [Dual-frame Fluid Motion Estimation with Test-time Optimization and Zero-divergence Loss](https://mlsanity.com/paper/2410.11934/) (2024)
- [CodeMerge: Codebook-Guided Model Merging for Robust Test-Time Adaptation in Autonomous Driving](https://mlsanity.com/paper/2505.16524/) (2025)
- [3D Test-time Adaptation via Graph Spectral Driven Point Shift](https://mlsanity.com/paper/2507.18225/) (2025)
- [DiffRGD: An Inference-Time Diffusion Guidance Through Riemannian Gradient Descent](https://mlsanity.com/paper/2606.28417/) (2026)
- [Everything to the Synthetic: Diffusion-driven Test-time Adaptation via Synthetic-Domain Alignment](https://mlsanity.com/paper/2406.04295/) (2024)
- [FOCUS: Frequency-Optimized Conditioning of DiffUSion Models for mitigating catastrophic forgetting during Test-Time Adaptation](https://mlsanity.com/paper/2508.14437/) (2025)
- [SteeringTTA: Guiding Diffusion Trajectories for Robust Test-Time-Adaptation](https://mlsanity.com/paper/2510.14634/) (2025)
- [FlowDec: Temporal Conditional Flow Decorruptor for Robust Continuous Vision-Language Navigation](https://mlsanity.com/paper/2606.22424/) (2026)
- [TestMate: Test-Time Domain Adaptation Aided by Lightweight Vision Foundation Model](https://mlsanity.com/paper/2607.03810/) (2026)

## Abstract (arXiv)

Machine learning models struggle with generalization when encountering out-of-distribution (OOD) samples with unexpected distribution shifts. For vision tasks, recent studies have shown that test-time adaptation employing diffusion models can achieve state-of-the-art accuracy improvements on OOD samples by generating new samples that align with the model's domain without the need to modify the model's weights. Unfortunately, those studies have primarily focused on pixel-level corruptions, thereby lacking the generalization to adapt to a broader range of OOD types. We introduce Generalized Diffusion Adaptation (GDA), a novel diffusion-based test-time adaptation method robust against diverse OOD types. Specifically, GDA iteratively guides the diffusion by applying a marginal entropy loss derived from the model, in conjunction with style and content preservation losses during the reverse sampling process. In other words, GDA considers the model's output behavior with the semantic information of the samples as a whole, which can reduce ambiguity in downstream tasks during the generation process. Evaluation across various popular model architectures and OOD benchmarks shows that GDA consistently outperforms prior work on diffusion-driven adaptation. Notably, it achieves the highest classification accuracy improvements, ranging from 4.4% to 5.02% on ImageNet-C and 2.5% to 7.4% on Rendition, Sketch, and Stylized benchmarks. This performance highlights GDA's generalization to a broader range of OOD benchmarks.