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ARXIV:2605.30825 · DIFFUSION MODELS · SUBMITTED 01 JUN · 20:32 UTC · FRESHNESS STALE
ARXIV:2605.30825DIFFUSION MODELSSUBMITTED 01 JUN · 20:32 UTCFRESHNESS STALEShervin Khalafi · Alejandro Ribeiro · Dongsheng Ding · arXiv
A unified framework for unlearning in diffusion models that minimizes deviation from pretrained models while preserving utility, using KL divergence and likelihood constraints.
Opportunity summary
Pain A unified framework for unlearning in diffusion models that minimizes deviation from pretrained models while preserving utility, using KL divergence and likelihood constraints.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A unified framework for unlearning in diffusion models that minimizes deviation from pretrained models while preserving utility, using KL divergence and likelihood constraints. We propose a principled constrained optimization framework that formulates unlearning as…
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…
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Experimental results demonstrate that our KL-constrained approach achieves superior retention-unlearning tradeoffs compared to weight-based baselines for concept and data unlearning, and that our likelihood-based…
Diffusion Models moved forward this cycle; last verified June 2026. Public score 3.0/10. Production flags indicate code availability.
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A unified framework for unlearning in diffusion models that minimizes deviation from pretrained models while preserving utility, using KL divergence and likelihood constraints.
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10.48550/arXiv.2605.30825A unified framework for unlearning in diffusion models that minimizes deviation from pretrained models while preserving utility, using KL divergence and likelihood constraints.
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 existing approaches for concept and data unlearning, while the third offers a novel and natural formulation for unlearning. Despite the nonconvexity of the KL constraints, we establish strong duality for all three problems, enabling us to explicitly characterize their optimal solutions as unlearning targets and develop primal-dual algorithms for each formulation. Experimental results demonstrate that our KL-constrained approach achieves superior retention-unlearning tradeoffs compared to weight-based baselines for concept and data unlearning, and that our likelihood-based approach matches unlearning effectiveness while better preserving retained concepts compared to baselines.
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PROBLEM
A unified framework for unlearning in diffusion models that minimizes deviation from pretrained models while preserving utility, using KL divergence and likelihood constraints. We propose a principled constrained optimization framework that formulates unlearning as minimizing th...
METHOD
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 devia...
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Experimental results demonstrate that our KL-constrained approach achieves superior retention-unlearning tradeoffs compared to weight-based baselines for concept and data unlearning, and that our likeliho...
WHY NOW
Diffusion Models moved forward this cycle; last verified June 2026. Public score 3.0/10. Production flags indicate code availability.
{"file name": "input.pdf", "number of pages": 27, "author": "Shervin Khalafi; Alejandro Ribeiro; Dongsheng Ding"
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A unified framework for unlearning in diffusion models that minimizes deviation from pretrained models while preserving utility, using KL divergence and likelihood constraints.
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