This is a curated list of research papers in Test-time Adaptation (TTA), which also goes by other names, such as Test-time Training (TTT), Source-free Domain Adaptation (SFDA) and Unsupervised Model Adaptation (UMA).
The repository is actively maintained. Pull requests or direct messages are welcome.
- Test-Time Training with Self-Supervision for Generalization under Distribution Shifts ICML'20 [Project]
- TTT++: When Does Self-Supervised Test-Time Training Fail or Thrive? NeurIPS'21 [Code]
- Model Adaptation: Historical Contrastive Learning for Unsupervised Domain Adaptation without Source Data NeurIPS'21
- Contrastive Test-Time Adaptation CVPR'22 [Code]
- Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning NeurIPS'22
- Test-Time Training with Masked Autoencoders NeurIPS'22 [Project]
- Improved Test-Time Adaptation for Domain Generalization CVPR'23 [Code]
- PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalization ICCV'23
- MATE: Masked Autoencoders are Online 3D Test-Time Learners ICCV'23
- On the Robustness of Open-World Test-Time Training: Self-Training with Dynamic Prototype Expansion ICCV'23 [Code]
- Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation ICML'20 [Code]
- Tent: Fully Test-Time Adaptation by Entropy Minimization ICLR'21 [Code]
- Uncertainty Reduction for Model Adaptation in Semantic Segmentation CVPR'21 [Code]
- Bayesian Adaptation for Covariate Shift NeurIPS'21
- Efficient Test-Time Model Adaptation without Forgetting ICML'22 [Code]
- Confidence Score for Source-Free Unsupervised Domain Adaptation ICML'22 [Code]
- Towards Stable Test-time Adaptation in Dynamic Wild World ICLR'23 [Code]
- Improving robustness against common corruptions by covariate shift adaptation NeurIPS'20 [Code]
- Tent: Fully Test-Time Adaptation by Entropy Minimization ICLR'21 [Code]
- Limitations of Post-Hoc Feature Alignment for Robustness CVPR'21 [Code]
- TTN: A Domain-Shift Aware Batch Normalization in Test-Time Adaptation ICLR'23
- Delta: Degradation-Free Fully Test-Time Adaptation ICLR'23
- Towards Stable Test-time Adaptation in Dynamic Wild World ICLR'23 [Code]
- Test-time Adaptation in the Dynamic World with Compound Domain Knowledge Management RA-L'23
- Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation ICML'20 [Code]
- Generative Pseudo-label Refinement for Unsupervised Domain Adaptation WACV'20
- A Free Lunch for Unsupervised Domain Adaptive Object Detection without Source Data AAAI'21
- Uncertainty Reduction for Model Adaptation in Semantic Segmentation CVPR'21 [Code]
- Adapting ImageNet-scale models to complex distribution shifts with self-learning TMLR'22 [Code]
- Continual Test-Time Domain Adaptation CVPR'22 [Code]
- Contrastive Test-Time Adaptation CVPR'22 [Code]
- Test-Time Adaptation via Conjugate Pseudo-labels NeurIPS'22 [Code]
- Towards Understanding GD with Hard and Conjugate Pseudo-labels for Test-Time Adaptation ICLR'23
- TeSLA: Test-Time Self-Learning With Automatic Adversarial Augmentation CVPR'23 [Code]
- Model Adaptation: Unsupervised Domain Adaptation Without Source Data CVPR'20
- Test-Time Classifier Adjustment Module for Model-Agnostic Domain Generalization NeurIPS'21 [Code]
- Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering NeurIPS'22 [Code]
- Attracting and Dispersing: A Simple Approach for Source-free Domain Adaptation NeurIPS'22 [Code]
- On the Robustness of Open-World Test-Time Training: Self-Training with Dynamic Prototype Expansion ICCV'23 [Code]
- SoFA: Source-data-free Feature Alignment for Unsupervised Domain Adaptation WACV'21
- Adaptive Adversarial Network for Source-Free Domain Adaptation ICCV'21
- TTT++: When Does Self-Supervised Test-Time Training Fail or Thrive? NeurIPS'21 [Code]
- Source-Free Adaptation to Measurement Shift via Bottom-Up Feature Restoration ICLR'22 [Code]
- Invariance Through Latent Alignment RSS'22 [Project]
- Source-Free Domain Adaptation via Distribution Estimation CVPR'22
- ActMAD: Activation Matching to Align Distributions for Test-Time-Training CVPR'23 [Code]
- Feature Alignment and Uniformity for Test Time Adaptation CVPR'23 [Code]
- Robustness to corruption in pre-trained Bayesian neural networks ICLR'23
- Model Adaptation: Unsupervised Domain Adaptation without Source Data CVPR'20
- Domain Impression: A Source Data Free Domain Adaptation Method WACV'21
- Back to the Source: Diffusion-Driven Test-Time Adaptation CVPR'23 [Code]
- Generalized Source-free Domain Adaptation ICCV'21 [Code]
- Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation NeurIPS'21 [Code]
- Test-Time Adaptation via Self-Training with Nearest Neighbor Information ICLR'23 [Code]
- MEMO: Test Time Robustness via Adaptation and Augmentation NeurIPS'22 [Code]
- Test time Adaptation through Perturbation Robustness NeurIPS-WS'21
- Balancing Discriminability and Transferability for Source-Free Domain Adaptation ICML'22 [Project]
- Test-Time Fast Adaptation for Dynamic Scene Deblurring via Meta-Auxiliary Learning CVPR'21
- Adaptive Risk Minimization: Learning to Adapt to Domain Shift NeurIPS'21 [Code]
- Learning to Generalize across Domains on Single Test Samples ICLR'22 [Code]
- Meta-DMoE: Adapting to Domain Shift by Meta-Distillation from Mixture-of-Experts NeurIPS'22 [Code]
- Continual Test-Time Domain Adaptation CVPR'22 [Code]
- NOTE: Robust Continual Test-time Adaptation Against Temporal Correlation NeurIPS'22 [Code]
- Extrapolative Continuous-time Bayesian Neural Network for Fast Training-free Test-time Adaptation NeurIPS'22 [Code]
- Decorate the Newcomers: Visual Domain Prompt for Continual Test Time Adaptation AAAI'23
- Robust Test-Time Adaptation in Dynamic Scenarios CVPR'23 [Code]
- A Probabilistic Framework for Lifelong Test-Time Adaptation CVPR'23 [Code]
- Robust Mean Teacher for Continual and Gradual Test-Time Adaptation CVPR'23 [Code]
- EcoTTA: Memory-Efficient Continual Test-time Adaptation via Self-distilled Regularization CVPR'23 [Project]
- Domain Adaptation in the Absence of Source Domain Data KDD'16
- Semantic Photo Manipulation with a Generative Image Prior SIGGRAPH'19
- Collaborative Sampling in Generative Adversarial Networks AAAI'20 [Code]
- Universal Source-Free Domain Adaptation CVPR'20 [Project]
- Adaptive Methods for Real-World Domain Generalization CVPR'21 [Code]
- Parameter-free Online Test-time Adaptation CVPR'22 [Code]
- Evaluating the Adversarial Robustness of Adaptive Test-time Defenses ICML'22
- MECTA: Memory-Economic Continual Test-Time Model Adaptation ICLR'23
- Neuro-Modulated Hebbian Learning for Fully Test-Time Adaptation CVPR'23
- On Pitfalls of Test-Time Adaptation ICML'2023 [Code]
- Consistent Video Depth Estimation SIGGRAPH'2020 [Project]
- Self-Supervised Policy Adaptation during Deployment ICLR'21 [Project]
- Source-Free Domain Adaptation for Image Segmentation MICCAI'20 [Code]
- Fully Test-Time Adaptation for Image Segmentation MICCAI'21
- Adapting Off-the-Shelf Source Segmenter for Target Medical Image Segmentation MICCAI'21
- Source-Free Domain Adaptation for Semantic Segmentation CVPR'21
- Generalize Then Adapt: Source-Free Domain Adaptive Semantic Segmentation ICCV'21 [Project]
- SS-SFDA: Self-Supervised Source-Free Domain Adaptation for Road Segmentation in Hazardous Environments ICCV'21 [Project]
- Test-Time Personalization with a Transformer for Human Pose Estimation NeurIPS'21 [Code]
- Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective CVPR'22 [Code]
- Ev-TTA: Test-Time Adaptation for Event-Based Object Recognition CVPR'22 [Code]
- MM-TTA: Multi-Modal Test-Time Adaptation for 3D Semantic Segmentation CVPR'22 [Project]
- Source-Free Object Detection by Learning to Overlook Domain Style CVPR'22 [Code]
- On the Road to Online Adaptation for Semantic Image Segmentation CVPR'22
- Test-Time Training Can Close the Natural Distribution Shift Performance Gap in Deep Learning Based Compressed Sensing ICML'22 [Code]
- The Dual Form of Neural Networks Revisited: Connecting Test Time Predictions to Training Patterns via Spotlights of Attention ICML'22 [Code]
- Source-free Video Domain Adaptation by Learning Temporal Consistency for Action Recognition ECCV'22 [Code]
- AuxAdapt: Stable and Efficient Test-Time Adaptation for Temporally Consistent Video Semantic Segmentation WACV'22
- Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models NeurIPS'22 [Project]
- TTA-COPE: Test-Time Adaptation for Category-Level Object Pose Estimation CVPR'23 [Project]
- SfM-TTR: Using Structure from Motion for Test-Time Refinement of Single-View Depth Networks CVPR'23 [Code]
- Video Test-Time Adaptation for Action Recognition CVPR'23 [Project]