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
- TTT++: When Does Self-Supervised Test-Time Training Fail or Thrive? NeurIPS'21
- Model Adaptation: Historical Contrastive Learning for Unsupervised Domain Adaptation without Source Data NeurIPS'21
- Contrastive Test-Time Adaptation CVPR'22
- Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning NeurIPS'22
- Test-Time Training with Masked Autoencoders NeurIPS'22
- Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation ICML'20
- Tent: Fully Test-Time Adaptation by Entropy Minimization ICLR'21
- Uncertainty Reduction for Model Adaptation in Semantic Segmentation CVPR'21
- Bayesian Adaptation for Covariate Shift NeurIPS'21
- Test-Time Adaptation to Distribution Shift by Confidence Maximization and Input Transformation Preprint'21
- Efficient Test-Time Model Adaptation without Forgetting ICML'22
- Confidence Score for Source-Free Unsupervised Domain Adaptation ICML'22
- Towards Stable Test-time Adaptation in Dynamic Wild World ICLR'23
- Improving robustness against common corruptions by covariate shift adaptation NeurIPS'20
- Tent: Fully Test-Time Adaptation by Entropy Minimization ICLR'21
- Limitations of Post-Hoc Feature Alignment for Robustness CVPR'21
- 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
- Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation ICML'20
- 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
- Adapting ImageNet-scale models to complex distribution shifts with self-learning Preprint'21
- Continual Test-Time Domain Adaptation CVPR'22
- Contrastive Test-Time Adaptation CVPR'22
- Test-Time Adaptation via Conjugate Pseudo-labels NeurIPS'22
- Towards Understanding GD with Hard and Conjugate Pseudo-labels for Test-Time Adaptation ICLR'23
- Model Adaptation: Unsupervised Domain Adaptation Without Source Data CVPR'20
- Test-Time Classifier Adjustment Module for Model-Agnostic Domain Generalization NeurIPS'21
- Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering NeurIPS'22
- Attracting and Dispersing: A Simple Approach for Source-free Domain Adaptation NeurIPS'22
- 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
- Source-Free Adaptation to Measurement Shift via Bottom-Up Feature Restoration ICLR'22
- Invariance Through Inference Preprint'21
- Source-Free Domain Adaptation via Distribution Estimation CVPR'22
- 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 Preprint'22
- Generalized Source-free Domain Adaptation ICCV'21
- Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation NeurIPS'21
- Test-Time Adaptation via Self-Training with Nearest Neighbor Information ICLR'23
- MEMO: Test Time Robustness via Adaptation and Augmentation NeurIPS'22
- Test time Adaptation through Perturbation Robustness NeurIPS-WS'21
- Balancing Discriminability and Transferability for Source-Free Domain Adaptation ICML'22
- 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
- Learning to Generalize across Domains on Single Test Samples ICLR'22
- Meta-DMoE: Adapting to Domain Shift by Meta-Distillation from Mixture-of-Experts NeurIPS'22
- Continual Test-Time Domain Adaptation CVPR'22
- NOTE: Robust Continual Test-time Adaptation Against Temporal Correlation NeurIPS'22
- Extrapolative Continuous-time Bayesian Neural Network for Fast Training-free Test-time Adaptation NeurIPS'22
- Decorate the Newcomers: Visual Domain Prompt for Continual Test Time Adaptation AAAI'23
- 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
- Universal Source-Free Domain Adaptation CVPR'20
- Adaptive Methods for Real-World Domain Generalization CVPR'21
- Parameter-free Online Test-time Adaptation CVPR'22
- Visual Prompt Tuning for Test-time Domain Adaptation Preprint'22
- Evaluating the Adversarial Robustness of Adaptive Test-time Defenses ICML'22
- MECTA: Memory-Economic Continual Test-Time Model Adaptation ICLR'23
- Self-Supervised Policy Adaptation during Deployment ICLR'21
- Source-Free Domain Adaptation for Image Segmentation MICCAI'20
- 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
- SS-SFDA: Self-Supervised Source-Free Domain Adaptation for Road Segmentation in Hazardous Environments ICCV'21
- Test-Time Personalization with a Transformer for Human Pose Estimation NeurIPS'21
- Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective CVPR'22
- Ev-TTA: Test-Time Adaptation for Event-Based Object Recognition CVPR'22
- MM-TTA: Multi-Modal Test-Time Adaptation for 3D Semantic Segmentation CVPR'22
- Source-Free Object Detection by Learning to Overlook Domain Style CVPR'22
- Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models NeurIPS'22
- Test-Time Training Can Close the Natural Distribution Shift Performance Gap in Deep Learning Based Compressed Sensing ICML'22
- The Dual Form of Neural Networks Revisited: Connecting Test Time Predictions to Training Patterns via Spotlights of Attention ICML'22
- AuxAdapt: Stable and Efficient Test-Time Adaptation for Temporally Consistent Video Semantic Segmentation WACV'22
- On the Road to Online Adaptation for Semantic Image Segmentation CVPR'22