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Memory augmented using diffusion model for class-incremental learning
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DOI：10.1016/j.imavis.2025.105600.png)
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Abstract
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• Generates synthetic samples of past classes using pre-trained diffusion models. • Synthetic data can be used for replay in supervised losses and for distillation loss. • Significantly improves performances of methods for class-incremental learning.
Keywords:
Continual learning
Class incremental learning
Image classification
Image generation
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