grypesc/AdaGauss
2024 Neurips paper on Continual Learning and Class Incremental Learning
This project helps machine learning researchers overcome the 'task-recency bias' problem when training models incrementally without storing old data. It takes a pre-trained feature extractor and an auxiliary neural network, along with a stream of new data, and outputs a more stable, incrementally trained model that retains knowledge of older classes better. It's designed for researchers working on advanced machine learning algorithms, specifically in continual learning.
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Use this if you are an ML researcher developing or experimenting with continual learning models, particularly in exemplar-free class incremental learning scenarios where you cannot store past data and need to mitigate task-recency bias.
Not ideal if you are looking for a plug-and-play solution for a business problem, or if you are not deeply involved in machine learning model development and research.
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Python
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Last pushed
May 21, 2025
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