arXiv:2509.21379v3 Announce Type: replace-cross Abstract: Concept unlearning in diffusion models is hampered by feature splitting, where concepts are distributed across many latent features, making their removal challenging and computationally expensive. We introduce SAEmnesia, a supervised sparse autoencoder framework that overcomes this by enforcing one-to-one concept-neuron mappings. By systematically labeling concepts during training, our method achieves feature centralization, binding each concept to a single, interpretable neuron. This enables highly targeted and efficient concept erasur
The proliferation of diffusion models and generative AI necessitates robust control mechanisms, especially as these models become more embedded in content creation and sensitive applications.
The ability to precisely unlearn or erase specific concepts from AI models is crucial for safety, ethical AI development, intellectual property, and mitigating biases and undesirable content.
This research introduces a more efficient and targeted method for concept removal in diffusion models, moving beyond current, computationally expensive and less precise techniques.
- · AI safety researchers
- · Generative AI platforms
- · Content moderation companies
- · Legal and compliance sectors
- · Malicious actors misusing generative AI
- · AI models with entrenched biases
- · Inefficient concept unlearning methods
Improved safety and control over synthetic media generated by diffusion models.
Reduced legal and ethical liabilities for companies deploying generative AI, accelerating adoption in regulated industries.
Potential for new business models focused on 'AI sanitization' or bespoke concept management services for AI.
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Read at arXiv cs.AI