Darth-Kronos/Unsupervised-Domain-Adaptation
Empirical evaluation and analysis of state-of-the-art methods for unsupervised domain adaptation on OFFICE-31 dataset, a benchmark dataset for visual domain adaptation.
This project offers a way for machine learning engineers to adapt their image classification models to new, unlabeled datasets. It takes an existing model trained on one set of images (like product photos from Amazon) and fine-tunes it to perform well on a different but related set of images (like photos from a webcam or DSLR), even without new labels. This helps maintain model performance when the real-world data distribution shifts.
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Use this if you have an image classification model that performs poorly when deployed in a new environment or with a new type of visual data because the data distribution is different from your original training data, and you lack new labeled data.
Not ideal if your problem does not involve visual data, or if you have plenty of labeled data available for your target domain.
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MIT
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Last pushed
Jul 21, 2023
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