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Degeneration-Tuning: Using Scrambled Grid shield Unwanted Concepts from Stable Diffusion
In this work, we propose a novel strategy named \textbf{Degeneration-Tuning (DT)} to shield contents of unwanted concepts from SD weights.
Continual Vision-Language Representation Learning with Off-Diagonal Information
Moreover, we empirically and theoretically demonstrate how SD leads to a performance decline for CLIP on cross-modal retrieval tasks.
Self-Supervised Class Incremental Learning
Existing Class Incremental Learning (CIL) methods are based on a supervised classification framework sensitive to data labels.
Revisiting Catastrophic Forgetting in Class Incremental Learning
After investigating existing strategies, we observe that there is a lack of study on how to prevent the inter-phase confusion.
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