Gorilla-Lab-SCUT/TRIBE
[AAAI 2024] Towards Real-World Test-Time Adaptation: Tri-Net Self-Training with Balanced Normalization
This project helps machine learning engineers and researchers improve the robustness of their image classification models when faced with real-world data shifts. It takes an existing image classification model and a stream of new, potentially corrupted or distribution-shifted images, then adapts the model to maintain high accuracy on this new data. The output is a more stable and accurate image classification model that can handle various types of real-world data streams.
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Use this if you need to ensure your deployed image classification models remain accurate and reliable even when encountering corrupted, noisy, or class-imbalanced images in live environments.
Not ideal if you are developing new image classification models from scratch or need to handle non-image data types.
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Language
Python
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Last pushed
Apr 08, 2025
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