A Unified Framework for Diffusion Model Unlearning with f-Divergence | Paper Citation Record

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This paper cites High-resolution image synthesis with latent diffusion models.

A Unified Framework for Diffusion Model Unlearning with f-Divergence High-resolution image synthesis with latent diffusion models

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This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

A Unified Framework for Diffusion Model Unlearning with f-Divergence Hierarchical Text-Conditional Image Generation with CLIP Latents

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This paper cites Photorealistic text-to-image diffusion models with deep language understanding.Advances in neural information processing systems, 35:36479–36494, 2022.

A Unified Framework for Diffusion Model Unlearning with f-Divergence Photorealistic text-to-image diffusion models with deep language understanding.Advances in neural information processing systems, 35:36479–36494, 2022

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This paper cites Laion-5b: An open large-scale dataset for training next generation image-text models.Advances in neural information processing systems, 35:25278–25294, 2022.

A Unified Framework for Diffusion Model Unlearning with f-Divergence Laion-5b: An open large-scale dataset for training next generation image-text models.Advances in neural information processing systems, 35:25278–25294, 2022

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A Unified Framework for Diffusion Model Unlearning with f-Divergence Safe latent diffusion: Mitigating inappropriate degeneration in diffusion models

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This paper cites Ai art and its impact on artists.

A Unified Framework for Diffusion Model Unlearning with f-Divergence Ai art and its impact on artists

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This paper cites Extracting training data from diffusion models.

A Unified Framework for Diffusion Model Unlearning with f-Divergence Extracting training data from diffusion models

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This paper cites Understanding and mitigating copying in diffusion models.Advances in Neural Information Processing Systems, 36:47783–47803, 2023.

A Unified Framework for Diffusion Model Unlearning with f-Divergence Understanding and mitigating copying in diffusion models.Advances in Neural Information Processing Systems, 36:47783–47803, 2023

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This paper cites Erasing concepts from diffusion models.

A Unified Framework for Diffusion Model Unlearning with f-Divergence Erasing concepts from diffusion models

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This paper cites Ablating concepts in text-to-image diffusion models.

A Unified Framework for Diffusion Model Unlearning with f-Divergence Ablating concepts in text-to-image diffusion models

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This paper cites Receler: Reliable concept erasing of text-to-image diffusion models via lightweight erasers.

A Unified Framework for Diffusion Model Unlearning with f-Divergence Receler: Reliable concept erasing of text-to-image diffusion models via lightweight erasers

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A Unified Framework for Diffusion Model Unlearning with f-Divergence Red-Teaming the Stable Diffusion Safety Filter

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This paper cites Safree: Training-free and adaptive guard for safe text-to-image and video generation.International Conference on Learning Representations, 2025.

A Unified Framework for Diffusion Model Unlearning with f-Divergence Safree: Training-free and adaptive guard for safe text-to-image and video generation.International Conference on Learning Representations, 2025

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This paper cites Erasing Undesirable Concepts in Diffusion Models with Adversarial Preservation.

A Unified Framework for Diffusion Model Unlearning with f-Divergence Erasing Undesirable Concepts in Diffusion Models with Adversarial Preservation

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This paper cites Fantastic Targets for Concept Erasure in Diffusion Models and Where To Find Them.

A Unified Framework for Diffusion Model Unlearning with f-Divergence Fantastic Targets for Concept Erasure in Diffusion Models and Where To Find Them

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A Unified Framework for Diffusion Model Unlearning with f-Divergence Fine-grained erasure in text-to-image diffusion-based foundation models

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A Unified Framework for Diffusion Model Unlearning with f-Divergence Unified concept editing in diffusion models

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This paper cites Reliable and efficient concept erasure of text-to-image diffusion models.

A Unified Framework for Diffusion Model Unlearning with f-Divergence Reliable and efficient concept erasure of text-to-image diffusion models

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This paper cites Mace: Mass concept erasure in diffusion models.

A Unified Framework for Diffusion Model Unlearning with f-Divergence Mace: Mass concept erasure in diffusion models

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This paper cites Minimalist concept erasure in generative models.

A Unified Framework for Diffusion Model Unlearning with f-Divergence Minimalist concept erasure in generative models

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This paper cites Unlearning concepts in diffusion model via concept domain correction and concept preserving gradient.

A Unified Framework for Diffusion Model Unlearning with f-Divergence Unlearning concepts in diffusion model via concept domain correction and concept preserving gradient

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This paper cites Generative adversarial nets.Advances in neural information processing systems, 27, 2014.

A Unified Framework for Diffusion Model Unlearning with f-Divergence Generative adversarial nets.Advances in neural information processing systems, 27, 2014

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Observation 633ae543-3b9e-4d04-a8f3-9a7a3deeec84 · outbound

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A Unified Framework for Diffusion Model Unlearning with f-Divergence Deep unsupervised learning using nonequilibrium thermodynamics

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A Unified Framework for Diffusion Model Unlearning with f-Divergence Denoising diffusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020

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A Unified Framework for Diffusion Model Unlearning with f-Divergence A general class of coefficients of divergence of one distribution from another.Journal of the Royal Statistical Society: Series B (Methodological), 28(1):131–142, 1966

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A Unified Framework for Diffusion Model Unlearning with f-Divergence On information-type measure of difference of probability distributions and indirect observations

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A Unified Framework for Diffusion Model Unlearning with f-Divergence Wainwright, and Michael I

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A Unified Framework for Diffusion Model Unlearning with f-Divergence Mode-seeking divergences: theory and applications to gans

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A Unified Framework for Diffusion Model Unlearning with f-Divergence The illusion of unlearning: The unstable nature of machine unlearning in text-to-image diffusion models

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A Unified Framework for Diffusion Model Unlearning with f-Divergence Flow Matching for Generative Modeling

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A Unified Framework for Diffusion Model Unlearning with f-Divergence Springer Verlag, 1985

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A Unified Framework for Diffusion Model Unlearning with f-Divergence Analyzingα-divergence in gaussian rate-distortion-perception theory

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A Unified Framework for Diffusion Model Unlearning with f-Divergence On the f-divergences between densities of a multivariate location or scale family.Statistics and Computing, 34(1):60, 2024

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A Unified Framework for Diffusion Model Unlearning with f-Divergence Gradient descent gan optimization is locally stable.Advances in neural information processing systems, 30, 2017

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A Unified Framework for Diffusion Model Unlearning with f-Divergence Which training methods for gans do actually converge? InInternational conference on machine learning, pages 3481–3490

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A Unified Framework for Diffusion Model Unlearning with f-Divergence Training deep energy-based models with f-divergence minimization

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A Unified Framework for Diffusion Model Unlearning with f-Divergence Unresolved cited work

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A Unified Framework for Diffusion Model Unlearning with f-Divergence f-gan: Training generative neural samplers using variational divergence minimization

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A Unified Framework for Diffusion Model Unlearning with f-Divergence One-step Diffusion Models with $f$-Divergence Distribution Matching

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A Unified Framework for Diffusion Model Unlearning with f-Divergence CLIPScore: A Reference-free Evaluation Metric for Image Captioning

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A Unified Framework for Diffusion Model Unlearning with f-Divergence Learning transferable visual models from natural language supervision

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A Unified Framework for Diffusion Model Unlearning with f-Divergence Sutherland, Michael Arbel, and Arthur Gretton

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A Unified Framework for Diffusion Model Unlearning with f-Divergence Towards making systems forget with machine unlearning

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A Unified Framework for Diffusion Model Unlearning with f-Divergence Machine unlearning

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A Unified Framework for Diffusion Model Unlearning with f-Divergence Eternal sunshine of the spotless net: Selective forgetting in deep networks

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A Unified Framework for Diffusion Model Unlearning with f-Divergence Fast yet effective machine unlearning.IEEE Transactions on Neural Networks and Learning Systems, 2023

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A Unified Framework for Diffusion Model Unlearning with f-Divergence Can bad teaching induce forgetting? unlearning in deep networks using an incompetent teacher

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A Unified Framework for Diffusion Model Unlearning with f-Divergence Zero-shot machine unlearning

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A Unified Framework for Diffusion Model Unlearning with f-Divergence Towards unbounded machine unlearning.Advances in neural information processing systems, 36:1957–1987, 2023

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A Unified Framework for Diffusion Model Unlearning with f-Divergence SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation

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A Unified Framework for Diffusion Model Unlearning with f-Divergence Fast machine unlearning without retraining through selective synaptic dampening

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A Unified Framework for Diffusion Model Unlearning with f-Divergence Distribution-level feature distancing for machine unlearning: Towards a better trade-off between model utility and forgetting

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A Unified Framework for Diffusion Model Unlearning with f-Divergence Lotus: Large-scale machine unlearning with a taste of uncertainty

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A Unified Framework for Diffusion Model Unlearning with f-Divergence f-scrub: Unbounded machine unlearning via f-divergences

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Observation af7844c2-9937-4fe3-82b3-ef031d10d835 · outbound

This paper cites Selective amnesia: A continual learning approach to forgetting in deep generative models.Advances in Neural Information Processing Systems, 36, 2024.

A Unified Framework for Diffusion Model Unlearning with f-Divergence Selective amnesia: A continual learning approach to forgetting in deep generative models.Advances in Neural Information Processing Systems, 36, 2024

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:51:19.495861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9b8436a0-d51f-4e4f-bbd1-855d2af3de05 · outbound

This paper cites Llm unlearning via loss adjustment with only forget data.International Conference on Learning Representations, 2025.

A Unified Framework for Diffusion Model Unlearning with f-Divergence Llm unlearning via loss adjustment with only forget data.International Conference on Learning Representations, 2025

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:51:19.479955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4affa2b7-0885-4548-a21c-d93603fa04ad · outbound

This paper cites Rethinking machine unlearning for large language models.Nature Machine Intelligence, pages 1–14, 2025.

A Unified Framework for Diffusion Model Unlearning with f-Divergence Rethinking machine unlearning for large language models.Nature Machine Intelligence, pages 1–14, 2025

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-15T15:51:18.595458Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93da194d-9077-41d0-ba5d-65a0a0bfda73 · outbound

This paper cites GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models.

A Unified Framework for Diffusion Model Unlearning with f-Divergence GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-15T15:51:18.599890Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2781354b-8e17-426d-a47b-a44512b1fc3c · outbound

This paper cites Diffusion art or digital forgery? investigating data replication in diffusion models.

A Unified Framework for Diffusion Model Unlearning with f-Divergence Diffusion art or digital forgery? investigating data replication in diffusion models

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-15T15:51:18.605072Z

Source-reported events for the cited work

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Observation 361cd9e5-858c-4b45-b085-396c9d715d9e · outbound

This paper cites One-dimensional adapter to rule them all: Concepts diffusion models and erasing applications.

A Unified Framework for Diffusion Model Unlearning with f-Divergence One-dimensional adapter to rule them all: Concepts diffusion models and erasing applications

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-15T15:51:18.610806Z

Source-reported events for the cited work

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Observation 9857e1f1-c045-411f-a29e-3cc7f2a4eee7 · outbound

This paper cites Editing implicit assumptions in text-to-image diffusion models.

A Unified Framework for Diffusion Model Unlearning with f-Divergence Editing implicit assumptions in text-to-image diffusion models

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-15T15:51:18.615382Z

Source-reported events for the cited work

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Observation c4ffc06f-a3f6-4571-9b10-8f68dcecb17e · outbound

This paper cites Mass-Editing Memory in a Transformer.

A Unified Framework for Diffusion Model Unlearning with f-Divergence Mass-Editing Memory in a Transformer

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-15T15:51:18.619954Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04872b87-460b-4b3d-9e0d-b5ac4d77394a · outbound

This paper cites Defensive unlearning with adversarial training for robust concept erasure in diffusion models.Advances in neural information processing systems, 37:36748–36776, 2024.

A Unified Framework for Diffusion Model Unlearning with f-Divergence Defensive unlearning with adversarial training for robust concept erasure in diffusion models.Advances in neural information processing systems, 37:36748–36776, 2024

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:51:19.283151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5ae6cb20-0ca0-40ec-ac01-a2dfb2790952 · outbound

This paper cites Efficient fine-tuning and concept suppression for pruned diffusion models.

A Unified Framework for Diffusion Model Unlearning with f-Divergence Efficient fine-tuning and concept suppression for pruned diffusion models

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-15T15:51:18.630829Z

Source-reported events for the cited work

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Observation 8e075863-c35f-4ffe-99b2-fe08f63d48e0 · outbound

This paper cites In International Conference on Machine Learning, pages 38448–38473.

A Unified Framework for Diffusion Model Unlearning with f-Divergence In International Conference on Machine Learning, pages 38448–38473

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:51:19.258178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4cf52afc-202d-4e8f-ae62-9f77200dfe4c · outbound

This paper cites When optimizingf-divergence is robust with label noise.

A Unified Framework for Diffusion Model Unlearning with f-Divergence When optimizingf-divergence is robust with label noise

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:51:19.243236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation bc8c78d6-cab0-4332-b6ed-3a5a0c362e06 · outbound

This paper cites Robust Classification with Noisy Labels Based on Posterior Maximization.

A Unified Framework for Diffusion Model Unlearning with f-Divergence Robust Classification with Noisy Labels Based on Posterior Maximization

Reference 67

Resolution: verified exact; local_arxiv, observed 2026-08-15T15:51:18.878079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a30b671b-e323-4a07-ae4d-83c9ed83215d · outbound

This paper cites Robust semi-supervised learning via f-divergence andα-rényi divergence.

A Unified Framework for Diffusion Model Unlearning with f-Divergence Robust semi-supervised learning via f-divergence andα-rényi divergence

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:51:19.227913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f813c11f-3f84-436d-a06a-a060e07cb8e6 · outbound

This paper cites Mutual information estimation via f- divergence and data derangements.Advances in Neural Information Processing Systems, 37:105114–105150, 2024.

A Unified Framework for Diffusion Model Unlearning with f-Divergence Mutual information estimation via f- divergence and data derangements.Advances in Neural Information Processing Systems, 37:105114–105150, 2024

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:51:19.212473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3d91d26a-8403-4172-affb-e1c530cbc949 · outbound

This paper cites Loss Functions and Operators Generated by f-Divergences.

A Unified Framework for Diffusion Model Unlearning with f-Divergence Loss Functions and Operators Generated by f-Divergences

Reference 70

Resolution: verified exact; local_arxiv, observed 2026-08-15T15:51:18.856421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8be65072-c2eb-42f4-bb25-9086b5b8f15f · outbound

This paper cites Fine-tuning of diffusion models via stochastic control: entropy regularization and beyond.arXiv preprint arXiv:2403.06279, 2024.

A Unified Framework for Diffusion Model Unlearning with f-Divergence Fine-tuning of diffusion models via stochastic control: entropy regularization and beyond.arXiv preprint arXiv:2403.06279, 2024

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-15T15:51:18.669117Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b3d4029-6249-43c0-bdeb-0a7c5aa146af · outbound

This paper cites Generalizing alignment paradigm of text-to-image generation with preferences through f-divergence minimization.

A Unified Framework for Diffusion Model Unlearning with f-Divergence Generalizing alignment paradigm of text-to-image generation with preferences through f-divergence minimization

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:51:19.195036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 18a96cd4-9bc4-4eee-ab04-09c67435702a · outbound

This paper cites Towards efficient machine unlearning with data augmentation: Guided loss-increasing (gli) to prevent the catastrophic model utility drop.

A Unified Framework for Diffusion Model Unlearning with f-Divergence Towards efficient machine unlearning with data augmentation: Guided loss-increasing (gli) to prevent the catastrophic model utility drop

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:51:19.179618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a35d7d46-ee12-4647-857d-4739be4cd1e8 · outbound

This paper cites Is retain set all you need in machine unlearning? restoring performance of unlearned models with out-of-distribution images.

A Unified Framework for Diffusion Model Unlearning with f-Divergence Is retain set all you need in machine unlearning? restoring performance of unlearned models with out-of-distribution images

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:51:19.162950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 25cc1216-c964-465f-b45d-4d31e2abc183 · outbound

This paper cites On the f-divergence and singularity of probability measures.Periodica Mathematica Hungarica, 2(1-4):223–234, 1972.

A Unified Framework for Diffusion Model Unlearning with f-Divergence On the f-divergence and singularity of probability measures.Periodica Mathematica Hungarica, 2(1-4):223–234, 1972

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:51:19.144647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c8c6de27-e2d5-481b-96d9-60c6fd066a70 · outbound

This paper cites Chapman and Hall/CRC, 2018.

A Unified Framework for Diffusion Model Unlearning with f-Divergence Chapman and Hall/CRC, 2018

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:51:19.127323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 17b105fa-690c-47c6-8997-ae380becab26 · outbound

This paper cites f-gan: Training generative neural samplers using variational divergence minimization.Advances in neural information processing systems, 29, 2016.

A Unified Framework for Diffusion Model Unlearning with f-Divergence f-gan: Training generative neural samplers using variational divergence minimization.Advances in neural information processing systems, 29, 2016

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-15T15:51:18.699330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e48355f6-682a-4970-9c6c-117c9ff3363d · outbound

This paper cites f-gail: Learning f-divergence for generative adversarial imitation learning.Advances in neural information processing systems, 33:12805–12815, 2020.

A Unified Framework for Diffusion Model Unlearning with f-Divergence f-gail: Learning f-divergence for generative adversarial imitation learning.Advances in neural information processing systems, 33:12805–12815, 2020

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:51:19.102560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ce761ea6-ee2a-47d5-a035-d4df393c3b21 · outbound

This paper cites concept erasing.

A Unified Framework for Diffusion Model Unlearning with f-Divergence concept erasing

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:51:19.086270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 132a3107-bc6e-4a25-b492-5ddc747c2995 · outbound

This paper cites Z x(0...ˆt−1) ˆtY t=1 pΦ(xt−1|xt,c) log pΦ(xˆt−1|xˆt,c) pˆΦ(xˆt−1|xˆt,c∗)dx(ˆt−1...0) # dx(ˆt...T) = Z x(ˆt...T) pΦ(x(ˆt...T)|c).

A Unified Framework for Diffusion Model Unlearning with f-Divergence Z x(0...ˆt−1) ˆtY t=1 pΦ(xt−1|xt,c) log pΦ(xˆt−1|xˆt,c) pˆΦ(xˆt−1|xˆt,c∗)dx(ˆt−1...0) # dx(ˆt...T) = Z x(ˆt...T) pΦ(x(ˆt...T)|c)

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:51:19.070625Z

Source-reported events for the cited work

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