ShallowUnlearn: Rethinking concept unlearning through disentangled component-level erasure in text-guided diffusion models

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Key Points

The study aims to improve concept unlearning in text-guided diffusion models by addressing ethical concerns related to harmful content generation.
Developed a shallow unlearning approach combining a component extraction module and a decoupling exchange strategy.
Pre-trained the component extraction module to decompose concept embeddings into distinct components.
Fine-tuned model weights to selectively remove critical erasure components while preserving non-essential components.
Shallow unlearning effectively balances the erasure of harmful concepts and the retention of model performance.
Demonstrated improved efficiency in forgetting harmful concepts with minimal impact on the model's utility.

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Investigates component-level erasure in text-guided diffusion models, suggesting improved concept unlearning efficacy.

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Zhao et al. (2026) studied this question.

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