Unlearning in Diffusion Models: A Unified Framework with KL Divergence and Likelihood Constraints
arXiv:2605.30825v1 Announce Type: new Abstract: Unlearning in diffusion models aims to remove undesirable data or concepts while preserving the utility of pretrained models -- two fundamentally conflicting objectives. We propose a principled constrained optimization framework that formulates unlearning as minimizing the deviation from a pretrained model, subject to explicit separation constraints from the unlearning distributions. Specifically, we formulate three constrained optimization problems based on reverse and forward KL divergences, and likelihood constraints. The first two generalize
The proliferation of powerful diffusion models and growing concerns about data privacy, intellectual property, and model governance necessitate advanced methods for 'unlearning' specific data or concepts from these models.
This research provides a foundational framework for controlling and curating the content of AI models, which is critical for compliance, ethical AI development, and mitigating risks associated with undesirable information or biases.
The ability to systematically unlearn information from diffusion models introduces a new dimension of control and adaptability, potentially improving model safety, regulatory compliance, and user trust.
- · AI model developers
- · Enterprises using diffusion models
- · Regulatory bodies
- · Data privacy advocates
- · Malicious actors attempting to exploit model weaknesses
- · Entities with poor data governance practices
Improved methods for removing sensitive or proprietary data from trained AI models will become standard practice.
This capability could lead to specialized 'unlearning as a service' offerings for AI model providers and users.
The legal and ethical frameworks around 'the right to be forgotten' could extend to engineered data within AI models, requiring proof of unlearning capabilities.
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Read at arXiv cs.LG