How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models | Paper Citation Record

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This paper cites ReFACT: Updating text-to-image models by editing the text encoder.

How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models ReFACT: Updating text-to-image models by editing the text encoder

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This paper cites Easily accessible text-to- image generation amplifies demographic stereotypes at large scale.

How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models Easily accessible text-to- image generation amplifies demographic stereotypes at large scale

Reference 2

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This paper cites Easily accessible text-to- image generation amplifies demographic stereotypes at large scale.

How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models Easily accessible text-to- image generation amplifies demographic stereotypes at large scale

Reference 3

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This paper cites FLUX.1-Schnell.

How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models FLUX.1-Schnell

Reference 4

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This paper cites Naruto blip captions.

How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models Naruto blip captions

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This paper cites Can editing llms inject harm?arXiv preprint arXiv:2407.20224, 2024.

How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models Can editing llms inject harm?arXiv preprint arXiv:2407.20224, 2024

Reference 6

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This paper cites Dall-eval: Probing the reasoning skills and social biases of text-to-image generation models.

How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models Dall-eval: Probing the reasoning skills and social biases of text-to-image generation models

Reference 7

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This paper cites Openbias: Open-set bias detection in text-to- image generative models.

How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models Openbias: Open-set bias detection in text-to- image generative models

Reference 8

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This paper cites Pure: Turning polysemantic neurons into pure features by identifying relevant circuits.

How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models Pure: Turning polysemantic neurons into pure features by identifying relevant circuits

Reference 9

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This paper cites Fleiss’ kappa statistic without paradoxes.

How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models Fleiss’ kappa statistic without paradoxes

Reference 10

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This paper cites Measuring nominal scale agreement among many raters.

How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models Measuring nominal scale agreement among many raters

Reference 11

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Observation 46564fd0-07a6-4e72-a089-4e6d31e78b4e · outbound

This paper cites Fair Diffusion: Instructing Text-to-Image Generation Models on Fairness.

How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models Fair Diffusion: Instructing Text-to-Image Generation Models on Fairness

Reference 12

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This paper cites Erasing concepts from diffusion models.

How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models Erasing concepts from diffusion models

Reference 13

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How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models Unified concept editing in diffusion models

Reference 14

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This paper cites Harm Amplification in Text-to-Image Models.

How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models Harm Amplification in Text-to-Image Models

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This paper cites CLIPScore: A Reference-free Evaluation Metric for Image Captioning.

How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models CLIPScore: A Reference-free Evaluation Metric for Image Captioning

Reference 16

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This paper cites Gans trained by a two time-scale update rule converge to a local nash equilibrium.

How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models Gans trained by a two time-scale update rule converge to a local nash equilibrium

Reference 17

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This paper cites Lora: Low-rank adaptation of large language models.

How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models Lora: Low-rank adaptation of large language models

Reference 18

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Observation d07b7e1a-265f-4b46-8e15-e2c1ab89bdec · outbound

This paper cites Harmful Fine-tuning Attacks and Defenses for Large Language Models: A Survey.

How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models Harmful Fine-tuning Attacks and Defenses for Large Language Models: A Survey

Reference 19

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This paper cites AI Alignment: A Comprehensive Survey.

How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models AI Alignment: A Comprehensive Survey

Reference 20

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This paper cites Rethinking Training for De-biasing Text-to-Image Generation: Unlocking the Potential of Stable Diffusion.

How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models Rethinking Training for De-biasing Text-to-Image Generation: Unlocking the Potential of Stable Diffusion

Reference 21

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This paper cites Safety Alignment Backfires: Preventing the Re-emergence of Suppressed Concepts in Fine-tuned Text-to-Image Diffusion Models.

How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models Safety Alignment Backfires: Preventing the Re-emergence of Suppressed Concepts in Fine-tuned Text-to-Image Diffusion Models

Reference 22

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This paper cites Overcoming catastrophic forgetting in neural networks.

How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models Overcoming catastrophic forgetting in neural networks

Reference 23

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This paper cites Self-discovering inter- pretable diffusion latent directions for responsible text-to-image generation.

How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models Self-discovering inter- pretable diffusion latent directions for responsible text-to-image generation

Reference 24

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This paper cites BLIP: Bootstrapping language-image pre-training for unified vision-language understanding and generation.

How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models BLIP: Bootstrapping language-image pre-training for unified vision-language understanding and generation

Reference 25

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This paper cites Word-Level Explanations for Analyzing Bias in Text-to-Image Models.

How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models Word-Level Explanations for Analyzing Bias in Text-to-Image Models

Reference 26

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This paper cites DoRA: Weight-Decomposed Low-Rank Adaptation.

How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models DoRA: Weight-Decomposed Low-Rank Adaptation

Reference 27

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This paper cites Catastrophic interference in connectionist networks: The sequential learning problem.

How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models Catastrophic interference in connectionist networks: The sequential learning problem

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This paper cites Locating and editing factual associations in GPT.

How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models Locating and editing factual associations in GPT

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How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models Mass editing memory in a transformer

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How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models Prodigy: an expeditiously adaptive parameter- free learner

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How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models Social biases through the text-to-image generation lens

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This paper cites Multifaceted Feature Visualization: Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networks.

How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models Multifaceted Feature Visualization: Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networks

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How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models Disentangling neuron representations with concept vectors

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How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models Editing implicit assumptions in text- to-image diffusion models

Reference 35

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How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

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How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models High- resolution image synthesis with latent diffusion models

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How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

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How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models Safe latent diffusion: Mitigating inappropriate degeneration in diffusion models

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How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models Safe latent diffusion: Mitigating inappropriate degeneration in diffusion models

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This paper cites LAION-5b: An open large-scale dataset for training next gen- eration image-text models.

How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models LAION-5b: An open large-scale dataset for training next gen- eration image-text models

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This paper cites Stablerep: Synthetic images from text-to-image models make strong visual representation learners.

How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models Stablerep: Synthetic images from text-to-image models make strong visual representation learners

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This paper cites Survey of Bias In Text-to-Image Generation: Definition, Evaluation, and Mitigation.

How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models Survey of Bias In Text-to-Image Generation: Definition, Evaluation, and Mitigation

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This paper cites Editing massive concepts in text-to-image diffusion models, 2024.

How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models Editing massive concepts in text-to-image diffusion models, 2024

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This paper cites Position: Edit- ing large language models poses serious safety risks.

How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models Position: Edit- ing large language models poses serious safety risks

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This paper cites A Comprehensive Study of Knowledge Editing for Large Language Models.

How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models A Comprehensive Study of Knowledge Editing for Large Language Models

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This paper cites Gender bias in coreference resolution: Evaluation and debiasing methods.

How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models Gender bias in coreference resolution: Evaluation and debiasing methods

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This paper cites Diffusion Tuning: Transferring Diffusion Models via Chain of Forgetting.

How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models Diffusion Tuning: Transferring Diffusion Models via Chain of Forgetting

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