arXiv:2509.21167v2 Announce Type: replace Abstract: Most existing methods for concept unlearning in text-to-image diffusion models minimize a mean squared error (MSE) loss between the denoiser outputs conditioned on a target and an anchor concept, which is implicitly the KL divergence between two Gaussians. We generalize this objective to any $f$-divergence, recovering MSE as the KL instance, and identify a family of $\alpha$-divergences whose Gaussian closed-form yields cheap, MSE-like training objectives. For the remaining $f$-divergences, we provide a min-max objective based on the variatio
The rapid advancement and widespread deployment of diffusion models necessitate robust methods for managing and controlling their learned knowledge, especially for unlearning specific concepts.
Improving the ability to unlearn specific concepts in AI models is crucial for ethical AI development, compliance with data regulations, and mitigating biases or unwanted behaviors.
The proposed unified framework using f-divergences offers a more flexible and potentially more effective approach to concept unlearning in diffusion models, moving beyond the current MSE/KL divergence limitations.
- · AI developers
- · Ethical AI research
- · Regulatory bodies
- · Malicious actors exploiting AI models
- · Systems with unmodifiable biases
More precise and efficient methods for removing undesirable information or behaviors from large AI models become available.
This could accelerate the deployment of diffusion models in sensitive applications where concept unlearning is a prerequisite.
The enhanced control over model knowledge might lead to new paradigms in AI safety and continuous model refinement, potentially impacting intellectual property considerations for AI-generated content.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG