corenel/pytorch-adda
A PyTorch implementation for Adversarial Discriminative Domain Adaptation
This project helps machine learning engineers or researchers adapt a trained model from one dataset to a similar but different dataset without extensive retraining. It takes an image classification model trained on a 'source' set of images (like MNIST handwritten digits) and adapts it to perform well on a 'target' set (like USPS handwritten digits), even if the target data looks slightly different. This is useful for researchers and ML engineers working with computer vision tasks.
493 stars. No commits in the last 6 months.
Use this if you have an image classification model that performs well on your existing training data, but struggles with similar images from a new source or domain, and you want to improve its performance on the new data without fully retraining from scratch.
Not ideal if you are looking for a general-purpose machine learning library or if your task involves domains completely unrelated to image classification.
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493
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141
Language
Python
License
MIT
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Last pushed
Apr 12, 2022
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