deepskies/SIDDA
SInkhorn Dynamic Domain Adaptation 🚰🎺
When you have images from different sources or conditions that a machine learning model struggles to classify accurately due to visual differences, this tool helps. It takes your image datasets from various 'domains' and outputs a more robust classification model. Data scientists, machine learning engineers, and researchers working with image classification across varying data sources would find this valuable.
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Use this if your image classification model performs poorly when applied to new datasets that look slightly different from your training data, and you want to improve its accuracy without extensive manual fine-tuning.
Not ideal if you are working with non-image data or if your classification problem does not involve domain shift issues.
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Last pushed
Aug 26, 2025
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