Introduction to Awesome Test-Time Adaptation
The "Awesome Test-Time Adaptation" project is a curated collection focusing on the adaptation techniques applied during the test-time phase. These techniques are designed to address issues related to domain shifts that occur when a model is tested on data that differs from the data it was trained on. The project gathers various resources and categorizes them based on specific types of adaptation, providing a comprehensive overview of the field.
Problem Overview
The project addresses a significant problem in machine learning known as domain shift, which involves the change of data distribution between the training and testing phases. This discrepancy can lead to a decline in model performance. Test-time adaptation aims to adjust the model during the testing phase to bridge this gap, enhancing its accuracy and reliability.
Categories of Test-Time Adaptation
The project organizes adaptation techniques into several categories, each addressing specific scenarios:
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Test-Time (Source-Free) Domain Adaptation (SFDA): This category focuses on adapting models to new domains without access to the source data. It is crucial when privacy concerns or data transfer issues restrict the availability of original training data.
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Test-Time Batch Adaptation (TTBA): In scenarios where data is available in batches, this technique allows adaptation at the batch level, adjusting models for each batch of testing data to account for variations.
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Test-Time Instance Adaptation (TTIA): This approach deals with adaptation at the individual instance level, ensuring that each test sample is handled distinctly based on its unique characteristics.
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Online Test-Time Adaptation (OTTA): Unlike offline methods, OTTA enables real-time adaptation as new data arrives, allowing models to evolve dynamically with changing data distributions.
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Test-Time Prior Adaptation (TTPA): In this category, models leverage prior information to aid in adaptation, enhancing predictive performance in situations where prior domain knowledge is available.
Datasets
The project also provides a list of commonly used datasets in the field of test-time adaptation. These datasets are accessible via a Google Sheets link, serving as a valuable resource for researchers looking to test and improve their adaptation techniques.
Contribution and Citation
The project is open to contributions from the community, encouraging researchers and developers to share their insights and advancements. For academic purposes, if this project's insights and resources prove beneficial, users are invited to cite the comprehensive survey associated with this repository:
@article{liang2023ttasurvey,
title={A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts},
author={Liang, Jian and He, Ran and Tan, Tieniu},
journal={International Journal Of Computer Vision},
year={2023}
}
In summary, "Awesome Test-Time Adaptation" serves as an invaluable resource for anyone interested in improving machine learning models' robustness against domain shifts. The project stands out by providing structured resources, promoting community collaboration, and offering access to foundational datasets in the field.