Paper Citation Record · LEDGER

CGCE: Classifier-Guided Concept Erasure in Generative Models

As of 19 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 1 inbound Pith citation observation for arXiv:2511.05865.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2511.05865 v3

Coverage vector

measured 56 of 56 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T23:27:52.714799Z

measured 57 of 57 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-01T05:35:48.896721Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-01T10:25:41.140061Z

Reference resolution

56 of 56 outbound references displayed

verified exact0
verified fuzzy0
unresolved56
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation be83c7dc-3a7e-4e90-af2c-acb903a38953 · outbound

This paper cites The pri- vacy onion effect: Memorization is relative.Advances in Neural Information Processing Systems, 35:13263–13276,.

CGCE: Classifier-Guided Concept Erasure in Generative Models The pri- vacy onion effect: Memorization is relative.Advances in Neural Information Processing Systems, 35:13263–13276,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:47.311741Z

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Observation ea819637-d4f8-40d0-9a8b-c5c672aa2974 · outbound

This paper cites Prompting4debugging: Red- teaming text-to-image diffusion models by finding prob- lematic prompts.

CGCE: Classifier-Guided Concept Erasure in Generative Models Prompting4debugging: Red- teaming text-to-image diffusion models by finding prob- lematic prompts

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:47.375311Z

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Observation 73e8f353-3dfe-4adb-b1eb-bf47017a23cc · outbound

This paper cites Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities.

CGCE: Classifier-Guided Concept Erasure in Generative Models Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:47.448123Z

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Observation 92697393-6a58-471f-bcc9-74b5bbcb844a · outbound

This paper cites Xtuner: A toolkit for efficiently fine-tuning llm.https://github.com/InternLM/ xtuner, 2023.

CGCE: Classifier-Guided Concept Erasure in Generative Models Xtuner: A toolkit for efficiently fine-tuning llm.https://github.com/InternLM/ xtuner, 2023

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:47.524591Z

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Observation e49485d2-b3c9-4b0c-82d9-7d06fa2eac69 · outbound

This paper cites Safesora: Towards safety alignment of text2video generation via a human pref- erence dataset.Advances in Neural Information Processing Systems, 37:17161–17214, 2024.

CGCE: Classifier-Guided Concept Erasure in Generative Models Safesora: Towards safety alignment of text2video generation via a human pref- erence dataset.Advances in Neural Information Processing Systems, 37:17161–17214, 2024

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:47.637673Z

Source-reported events for the cited work

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Observation 300fcc04-3ec6-498b-8bd8-d9af2d51f184 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

CGCE: Classifier-Guided Concept Erasure in Generative Models Imagenet: A large-scale hierarchical image database

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:47.722726Z

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Observation bd4f8f76-c42f-436a-affa-17b5a012ddab · outbound

This paper cites Scaling recti- fied flow transformers for high-resolution image synthesis.

CGCE: Classifier-Guided Concept Erasure in Generative Models Scaling recti- fied flow transformers for high-resolution image synthesis

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:47.809373Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe28814d-9ead-440f-9ae4-fe83e45a4a56 · outbound

This paper cites Erasing concepts from diffusion models.

CGCE: Classifier-Guided Concept Erasure in Generative Models Erasing concepts from diffusion models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:47.877015Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36408fdb-4339-4193-b749-a353de8ab4aa · outbound

This paper cites Unified concept editing in diffusion models.

CGCE: Classifier-Guided Concept Erasure in Generative Models Unified concept editing in diffusion models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:47.998901Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ef0474c-fadd-483b-89c3-f1f7d0fc9d13 · outbound

This paper cites Eraseanything: Enabling concept erasure in rectified flow transformers.

CGCE: Classifier-Guided Concept Erasure in Generative Models Eraseanything: Enabling concept erasure in rectified flow transformers

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:48.124268Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a35b930-caa4-492a-b419-8f8fc653865f · outbound

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

CGCE: Classifier-Guided Concept Erasure in Generative Models Reliable and efficient concept erasure of text-to- image diffusion models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:48.200365Z

Source-reported events for the cited work

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Observation 97c909ca-fb0f-42c1-92d5-3476da735502 · outbound

This paper cites Infinity: Scaling bit- wise autoregressive modeling for high-resolution image syn- thesis.

CGCE: Classifier-Guided Concept Erasure in Generative Models Infinity: Scaling bit- wise autoregressive modeling for high-resolution image syn- thesis

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:48.316310Z

Source-reported events for the cited work

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Observation 78148681-b060-4bfb-8d45-8fcd7687004e · outbound

This paper cites Selective amnesia: A contin- ual learning approach to forgetting in deep generative mod- els.Advances in Neural Information Processing Systems, 36: 17170–17194, 2023.

CGCE: Classifier-Guided Concept Erasure in Generative Models Selective amnesia: A contin- ual learning approach to forgetting in deep generative mod- els.Advances in Neural Information Processing Systems, 36: 17170–17194, 2023

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:48.426839Z

Source-reported events for the cited work

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Observation 630d3723-768a-4eab-bcf2-921465c68876 · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilib- rium.Advances in neural information processing systems, 30, 2017.

CGCE: Classifier-Guided Concept Erasure in Generative Models Gans trained by a two time-scale update rule converge to a local nash equilib- rium.Advances in neural information processing systems, 30, 2017

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:48.512070Z

Source-reported events for the cited work

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Observation 3dedbf4d-6647-4322-b7a3-ce6f30f10e92 · outbound

This paper cites Classifier-Free Diffusion Guidance.

CGCE: Classifier-Guided Concept Erasure in Generative Models Classifier-Free Diffusion Guidance

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:48.608897Z

Source-reported events for the cited work

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Observation a32d544e-597e-4523-ac14-38f4981a7cc7 · outbound

This paper cites Imagen Video: High Definition Video Generation with Diffusion Models.

CGCE: Classifier-Guided Concept Erasure in Generative Models Imagen Video: High Definition Video Generation with Diffusion Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:48.611850Z

Source-reported events for the cited work

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Observation da8000fe-4d19-41f6-80ea-97d144abab76 · outbound

This paper cites Race: Ro- bust adversarial concept erasure for secure text-to-image dif- fusion model.

CGCE: Classifier-Guided Concept Erasure in Generative Models Race: Ro- bust adversarial concept erasure for secure text-to-image dif- fusion model

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:48.691861Z

Source-reported events for the cited work

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Observation 6b19ba5a-aac7-4f23-9844-69623a89d02d · outbound

This paper cites HunyuanVideo: A Systematic Framework For Large Video Generative Models.

CGCE: Classifier-Guided Concept Erasure in Generative Models HunyuanVideo: A Systematic Framework For Large Video Generative Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:48.846912Z

Source-reported events for the cited work

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Observation c8f2921c-a8c0-40a1-8602-a6cbce7b0d2c · outbound

This paper cites Eraseflow: Learn- ing concept erasure policies via gflownet-driven alignment.

CGCE: Classifier-Guided Concept Erasure in Generative Models Eraseflow: Learn- ing concept erasure policies via gflownet-driven alignment

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:48.910268Z

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Observation 0fbe14ff-56a9-4de6-8264-573370277212 · outbound

This paper cites Flux.https://github.com/ black-forest-labs/flux, 2024.

CGCE: Classifier-Guided Concept Erasure in Generative Models Flux.https://github.com/ black-forest-labs/flux, 2024

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:48.989983Z

Source-reported events for the cited work

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Observation 0697d364-8c55-4447-9775-86010a3e3243 · outbound

This paper cites Microsoft coco: Common objects in context.

CGCE: Classifier-Guided Concept Erasure in Generative Models Microsoft coco: Common objects in context

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:49.052076Z

Source-reported events for the cited work

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Observation 3524ccc0-31c1-46eb-a204-10d8616eeb31 · outbound

This paper cites Mace: Mass concept erasure in diffu- sion models.

CGCE: Classifier-Guided Concept Erasure in Generative Models Mace: Mass concept erasure in diffu- sion models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:49.163869Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 637ffa6c-c0ca-45aa-a247-cc9397101590 · outbound

This paper cites Nudenet: Neural nets for nudity classification, detection, and selective censoring.https://github.

CGCE: Classifier-Guided Concept Erasure in Generative Models Nudenet: Neural nets for nudity classification, detection, and selective censoring.https://github

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:49.227067Z

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Observation f61bcdb0-413f-4dc9-9d56-cf8729f72809 · outbound

This paper cites Direct unlearning optimization for robust and safe text- to-image models.Advances in Neural Information Process- ing Systems, 37:80244–80267, 2024.

CGCE: Classifier-Guided Concept Erasure in Generative Models Direct unlearning optimization for robust and safe text- to-image models.Advances in Neural Information Process- ing Systems, 37:80244–80267, 2024

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:49.295307Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5933b972-86be-4cd7-b1f8-ae4efa4f75c9 · outbound

This paper cites SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis.

CGCE: Classifier-Guided Concept Erasure in Generative Models SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:49.373552Z

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Observation 6feb786b-af3e-4ee4-8b47-5fbc07553dba · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

CGCE: Classifier-Guided Concept Erasure in Generative Models Learning transferable visual models from natural language supervi- sion

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:49.452395Z

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Observation 91ae27e3-b1a1-40cb-b49b-bccf38f689c3 · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.Journal of machine learning research, 21(140):1–67, 2020.

CGCE: Classifier-Guided Concept Erasure in Generative Models Exploring the limits of transfer learning with a unified text-to-text transformer.Journal of machine learning research, 21(140):1–67, 2020

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:49.516502Z

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Observation 7c77d528-0f29-4e7f-b183-26d8c65c3766 · outbound

This paper cites Six-cd: Benchmarking concept removals for benign text-to-image diffusion models, 2025.

CGCE: Classifier-Guided Concept Erasure in Generative Models Six-cd: Benchmarking concept removals for benign text-to-image diffusion models, 2025

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:49.563595Z

Source-reported events for the cited work

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Observation fd809c59-a8e2-492b-a7be-d6dd8ef5687b · outbound

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

CGCE: Classifier-Guided Concept Erasure in Generative Models High-resolution image synthesis with latent diffusion models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:49.619751Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08c9ae99-f7b7-40fa-a8f5-ba14c687caa9 · outbound

This paper cites Safe latent diffusion: Mitigating inappro- priate degeneration in diffusion models.

CGCE: Classifier-Guided Concept Erasure in Generative Models Safe latent diffusion: Mitigating inappro- priate degeneration in diffusion models

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:49.710577Z

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Observation 4242221a-fee6-4a0b-9707-b61f58131716 · outbound

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

CGCE: Classifier-Guided Concept Erasure in Generative Models Laion-5b: An open large-scale dataset for training next generation image-text models.Advances in neural in- formation processing systems, 35:25278–25294, 2022

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:49.769659Z

Source-reported events for the cited work

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Observation 62c4b662-a9d0-46c8-924e-839d2e9f01f4 · outbound

This paper cites Stereo: A two- stage framework for adversarially robust concept erasing from text-to-image diffusion models.

CGCE: Classifier-Guided Concept Erasure in Generative Models Stereo: A two- stage framework for adversarially robust concept erasing from text-to-image diffusion models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:49.847704Z

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Unavailable: canonical work link unavailable.

Observation cc9f9d82-bf2e-450c-934e-051b3b81159c · outbound

This paper cites Qwen2.5: A party of foundation models, 2024.

CGCE: Classifier-Guided Concept Erasure in Generative Models Qwen2.5: A party of foundation models, 2024

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:49.985301Z

Source-reported events for the cited work

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Observation 118f526a-c9cf-47e1-b905-8c2fc7aca85a · outbound

This paper cites Attention is all you need.Advances in neural information processing systems, 30, 2017.

CGCE: Classifier-Guided Concept Erasure in Generative Models Attention is all you need.Advances in neural information processing systems, 30, 2017

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:50.099147Z

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Observation 30ff74b4-8af9-426b-81dc-71df5e6cf7e0 · outbound

This paper cites Switti: Designing Scale-Wise Transformers for Text-to-Image Synthesis.

CGCE: Classifier-Guided Concept Erasure in Generative Models Switti: Designing Scale-Wise Transformers for Text-to-Image Synthesis

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:50.193473Z

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Observation 22ee8b16-b3d2-41a8-a60f-77c750beb220 · outbound

This paper cites VideoEraser: Concept Erasure in Text-to-Video Diffusion Models.

CGCE: Classifier-Guided Concept Erasure in Generative Models VideoEraser: Concept Erasure in Text-to-Video Diffusion Models

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:50.310190Z

Source-reported events for the cited work

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Observation 869a380e-7321-453d-aa21-43b2c90193c5 · outbound

This paper cites Mma-diffusion: Multimodal attack on diffusion models.

CGCE: Classifier-Guided Concept Erasure in Generative Models Mma-diffusion: Multimodal attack on diffusion models

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:50.391985Z

Source-reported events for the cited work

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Observation 4f43a05c-1b45-43fe-8cf5-141a2234d4e9 · outbound

This paper cites CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer.

CGCE: Classifier-Guided Concept Erasure in Generative Models CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:50.494920Z

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Observation 2e5111c4-b796-4134-8dc3-1812de519698 · outbound

This paper cites T2vunlearning: A concept erasing method for text-to-video diffusion models.arXiv preprint arXiv:2505.17550, 2025.

CGCE: Classifier-Guided Concept Erasure in Generative Models T2vunlearning: A concept erasing method for text-to-video diffusion models.arXiv preprint arXiv:2505.17550, 2025

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:50.603012Z

Source-reported events for the cited work

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Observation c3419b20-9333-4791-a216-67bd5a3a898b · outbound

This paper cites Safree: Training-free and adaptive guard for safe text-to-image and video generation.ICLR, 2025.

CGCE: Classifier-Guided Concept Erasure in Generative Models Safree: Training-free and adaptive guard for safe text-to-image and video generation.ICLR, 2025

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:50.704857Z

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Unavailable: canonical work link unavailable.

Observation 6ddc1ee7-234b-42f4-aeb4-40eaa837fd1d · outbound

This paper cites Ring-a-bell! how reliable are concept removal methods for diffusion models? InThe Twelfth International Conference on Learning Representations, 2024.

CGCE: Classifier-Guided Concept Erasure in Generative Models Ring-a-bell! how reliable are concept removal methods for diffusion models? InThe Twelfth International Conference on Learning Representations, 2024

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:50.803060Z

Source-reported events for the cited work

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Observation 81d7c2b6-5a23-408c-bdd0-9d47b495e6d9 · outbound

This paper cites Forget-me-not: Learning to for- get in text-to-image diffusion models.

CGCE: Classifier-Guided Concept Erasure in Generative Models Forget-me-not: Learning to for- get in text-to-image diffusion models

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:51.022115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01460627-c266-4f46-9a23-e7278b665e2f · outbound

This paper cites The unreasonable effectiveness of deep features as a perceptual metric.

CGCE: Classifier-Guided Concept Erasure in Generative Models The unreasonable effectiveness of deep features as a perceptual metric

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:51.107495Z

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Unavailable: canonical work link unavailable.

Observation ea815c81-48f5-4ca4-87fb-e4c749d173f8 · outbound

This paper cites To gener- ate or not? safety-driven unlearned diffusion models are still easy to generate unsafe images.

CGCE: Classifier-Guided Concept Erasure in Generative Models To gener- ate or not? safety-driven unlearned diffusion models are still easy to generate unsafe images

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:51.214086Z

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Observation 9b3de19b-bcea-494f-8ee3-46e27172ee49 · outbound

This paper cites The al- gorithm iteratively refines a prompt’s text embeddingε p, overKsteps.

CGCE: Classifier-Guided Concept Erasure in Generative Models The al- gorithm iteratively refines a prompt’s text embeddingε p, overKsteps

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:51.356194Z

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Observation 5d00946f-7c2e-47d6-a9f0-8e362f7c9fba · outbound

This paper cites Prompt Template To ensure training data of our classifier diversity, we utilized multiple LLMs, including Gemini 2.5 Pro [3] and Qwen2.5- 7B-Instruct [33].

CGCE: Classifier-Guided Concept Erasure in Generative Models Prompt Template To ensure training data of our classifier diversity, we utilized multiple LLMs, including Gemini 2.5 Pro [3] and Qwen2.5- 7B-Instruct [33]

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:51.520092Z

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Observation 7737b263-23ff-4621-8bed-0d24e34ede08 · outbound

This paper cites an unresolved cited work.

CGCE: Classifier-Guided Concept Erasure in Generative Models Unresolved cited work

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:51.643715Z

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Observation f60d3f4e-2f47-4832-b6a7-fae327cecc5a · outbound

This paper cites Van Gogh.

CGCE: Classifier-Guided Concept Erasure in Generative Models Van Gogh

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:51.760020Z

Source-reported events for the cited work

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Observation 6a4b539a-cb32-4c4e-9343-293a11390ce4 · outbound

This paper cites an unresolved cited work.

CGCE: Classifier-Guided Concept Erasure in Generative Models Unresolved cited work

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:51.840341Z

Source-reported events for the cited work

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Observation 8371198e-0bfd-47cc-80cb-73bad3ff834d · outbound

This paper cites The prompts within each pair should be as similar as possible in structure and content, with the main difference being thesexualelements.

CGCE: Classifier-Guided Concept Erasure in Generative Models The prompts within each pair should be as similar as possible in structure and content, with the main difference being thesexualelements

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:52.012144Z

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Observation ab1d54f2-4509-4128-8e3b-c0b51ea2a76a · outbound

This paper cites an unresolved cited work.

CGCE: Classifier-Guided Concept Erasure in Generative Models Unresolved cited work

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:52.128100Z

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Observation 4dbe12d8-b225-4e67-9bbb-08a627870698 · outbound

This paper cites church”. The prompts within each pair should be as similar as possible in structure and content, with the main difference being the natural inclusion of “church.

CGCE: Classifier-Guided Concept Erasure in Generative Models church”. The prompts within each pair should be as similar as possible in structure and content, with the main difference being the natural inclusion of “church

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-03T23:27:52.246281Z

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Observation 15e60996-1614-4044-889b-648ccff58e54 · outbound

This paper cites Image in the style of{artist name}.

CGCE: Classifier-Guided Concept Erasure in Generative Models Image in the style of{artist name}

Reference 53

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Observation 8141dccb-681b-4e70-82d0-b3344be7bd95 · outbound

This paper cites Fig- ure 6 provides a qualitative comparison for these scenarios.

CGCE: Classifier-Guided Concept Erasure in Generative Models Fig- ure 6 provides a qualitative comparison for these scenarios

Reference 54

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Observation 460c9ca7-4cc7-468a-86a2-36ac28a9bc0a · outbound

This paper cites As shown in Tab.

CGCE: Classifier-Guided Concept Erasure in Generative Models As shown in Tab

Reference 55

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Observation 8231636f-f25b-40bc-858c-89057d709af8 · outbound

This paper cites Van Gogh.

CGCE: Classifier-Guided Concept Erasure in Generative Models Van Gogh

Reference 56

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Pith citing papers

Observation f867ec7f-0350-49ca-b71f-43f5fcae0e0f · inbound

Cross-Space Distillation: Teaching One-Step Students with Modern Diffusion Teachers cites this paper.

Cross-Space Distillation: Teaching One-Step Students with Modern Diffusion Teachers CGCE: Classifier-Guided Concept Erasure in Generative Models

Reference 34

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