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Semantic Prompting with Image-Token for Continual Learning
Continual learning aims to refine model parameters for new tasks while retaining knowledge from previous tasks.
SRIL: Selective Regularization for Class-Incremental Learning
We also propose a method to selectively interpolate the weight of the previous model for a balance between stability and plasticity, and we adjust whether to transfer through model confidence to ensure the performance of the previous class and enable exploratory learning.
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