SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation | Paper Citation Record

Observation 89d37537-98c7-465b-b063-0a8792890f6e · outbound

This paper cites Deep learning with differential privacy.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Deep learning with differential privacy

Reference 1

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Observation d1386841-bf6f-4f2f-b075-c9bdd6b57eb6 · outbound

This paper cites Big healthcare data: preserving secu- rity and privacy.Journal of big data, 5(1):1–18, 2018.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Big healthcare data: preserving secu- rity and privacy.Journal of big data, 5(1):1–18, 2018

Reference 2

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Observation 1b3c5248-763b-47f7-a327-fc8c11133734 · outbound

This paper cites Evaluations of Machine Learning Privacy Defenses are Misleading.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Evaluations of Machine Learning Privacy Defenses are Misleading

Reference 3

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Observation 9cc63707-d08a-44ff-9585-111424626013 · outbound

This paper cites Wasserstein gan, 2017.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Wasserstein gan, 2017

Reference 4

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Observation f41295a2-7a26-4092-88b0-7623c5482608 · outbound

This paper cites Feedback-guided data synthesis for imbal- anced classification.arXiv e-prints, pages arXiv–2310, 2023.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Feedback-guided data synthesis for imbal- anced classification.arXiv e-prints, pages arXiv–2310, 2023

Reference 5

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Observation 47646c47-9473-4b56-bc35-15254a0143f4 · outbound

This paper cites Automatic Discovery of Privacy-Utility Pareto Fronts.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Automatic Discovery of Privacy-Utility Pareto Fronts

Reference 6

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Observation c7c96911-2d93-401a-a881-0fe0b46bf0d9 · outbound

This paper cites Synthetic Data from Diffusion Models Improves ImageNet Classification.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Synthetic Data from Diffusion Models Improves ImageNet Classification

Reference 7

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Observation 02e8642f-1236-49c6-8971-85efb472448b · outbound

This paper cites Differential privacy has disparate impact on model accuracy.Advances in neural information processing systems, 32, 2019.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Differential privacy has disparate impact on model accuracy.Advances in neural information processing systems, 32, 2019

Reference 8

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Observation b84477ab-ff64-4428-b6b1-5bd82d927ac0 · outbound

This paper cites Leaving Reality to Imagination: Robust Classification via Generated Datasets.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Leaving Reality to Imagination: Robust Classification via Generated Datasets

Reference 9

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Observation 04339a51-06e0-458c-9416-1513a43d58e3 · outbound

This paper cites Conditional Image Generation with Score-Based Diffusion Models.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Conditional Image Generation with Score-Based Diffusion Models

Reference 10

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Observation a38a2a2e-acf9-429b-b9bb-aaea207a986d · outbound

This paper cites Privacy in Social Media: Identification, Mitigation and Applications.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Privacy in Social Media: Identification, Mitigation and Applications

Reference 11

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Observation 66e9a5db-47d1-430b-bd2c-ec6e41cb6648 · outbound

This paper cites Private GANs, Revisited.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Private GANs, Revisited

Reference 12

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Observation b0ff5968-3f05-4a5a-8685-5ef33f036e92 · outbound

This paper cites Large Scale GAN Training for High Fidelity Natural Image Synthesis.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Large Scale GAN Training for High Fidelity Natural Image Synthesis

Reference 13

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Observation 50a6fff5-2863-4078-afd3-f16e3ebe052e · outbound

This paper cites Instructpix2pix: Learning to follow image editing in- structions.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Instructpix2pix: Learning to follow image editing in- structions

Reference 14

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Observation 342413ad-9f95-4858-bcd1-bb63cc01afe4 · outbound

This paper cites Don’t generate me: Training differen- tially private generative models with sinkhorn diver- gence.Advances in Neural Information Processing Systems, 34:12480–12492, 2021.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Don’t generate me: Training differen- tially private generative models with sinkhorn diver- gence.Advances in Neural Information Processing Systems, 34:12480–12492, 2021

Reference 15

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Observation fa996c9e-c958-417c-bd99-26a61f6fd044 · outbound

This paper cites Member- ship inference attacks from first principles.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Member- ship inference attacks from first principles

Reference 16

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Observation ff29b0e9-6fcc-4464-8881-0d1de34ea30a · outbound

This paper cites Dataset distil- lation by matching training trajectories.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Dataset distil- lation by matching training trajectories

Reference 17

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Observation 7426592f-d395-442c-b463-3154c71b3052 · outbound

This paper cites Gs-wgan: A gradient-sanitized approach for learning differentially private generators.Advances in Neural Information Processing Systems, 33:12673– 12684, 2020.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Gs-wgan: A gradient-sanitized approach for learning differentially private generators.Advances in Neural Information Processing Systems, 33:12673– 12684, 2020

Reference 18

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Observation 82ee82de-d46d-48fd-9df7-f7ce76bedb31 · outbound

This paper cites Gan-leaks: A taxonomy of membership inference at- tacks against generative models.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Gan-leaks: A taxonomy of membership inference at- tacks against generative models

Reference 19

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Observation b6c74020-e01e-46a5-9cbf-0cc7d515a1a7 · outbound

This paper cites Dpgen: Differentially private generative energy-guided network for natural image synthesis.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Dpgen: Differentially private generative energy-guided network for natural image synthesis

Reference 20

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Observation 0549d354-f7d7-4f46-b602-d7a5ea4f7392 · outbound

This paper cites Variational Lossy Autoencoder.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Variational Lossy Autoencoder

Reference 21

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Observation dad4e1d9-89d1-4636-b8e9-6a6c3911c8e5 · outbound

This paper cites Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images

Reference 22

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Observation 4d414046-3f3a-4b17-86ad-48e5536c2fda · outbound

This paper cites Label-only membership inference attacks.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Label-only membership inference attacks

Reference 23

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Observation 2ea2ffd0-c9ae-43cf-bd6f-7a699fbca190 · outbound

This paper cites On the Vulnerability of Data Points under Multiple Membership Inference Attacks and Target Models.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation On the Vulnerability of Data Points under Multiple Membership Inference Attacks and Target Models

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Observation 50bd7bd5-2f4a-4e27-b3d9-d15dc2a2f617 · outbound

This paper cites End- to-end sinkhorn autoencoder with noise generator.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation End- to-end sinkhorn autoencoder with noise generator

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Observation ccd24067-9cb1-4082-a384-bdd0c64d3203 · outbound

This paper cites Diffusion models beat gans on image synthesis.Advances in neural information processing systems, 34:8780–8794, 2021.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Diffusion models beat gans on image synthesis.Advances in neural information processing systems, 34:8780–8794, 2021

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Observation 624238ec-8d6f-4c84-adf5-fec94b8d82f7 · outbound

This paper cites Differentially Private Diffusion Models.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Differentially Private Diffusion Models

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Observation a5a086b2-8bd0-4bd2-9917-9679b2c11cde · outbound

This paper cites Are diffusion models vulnerable to membership inference attacks? InInternational Conference on Machine Learning, pages 8717–8730.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Are diffusion models vulnerable to membership inference attacks? InInternational Conference on Machine Learning, pages 8717–8730

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Observation 367ab0c8-0b73-4777-8870-b7b8c0331b84 · outbound

This paper cites an unresolved cited work.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Unresolved cited work

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Observation adfb1bf0-cbf8-4166-9b6e-ed34958b8e12 · outbound

This paper cites Calibrating noise to sensitivity in pri- vate data analysis.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Calibrating noise to sensitivity in pri- vate data analysis

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Observation 76bb79f6-939b-48cc-987a-36fbd34ed4ae · outbound

This paper cites The algorithmic foundations of differential privacy.Foundations and Trends® in Theoretical Computer Science, 9(3–4):211– 407, 2014.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation The algorithmic foundations of differential privacy.Foundations and Trends® in Theoretical Computer Science, 9(3–4):211– 407, 2014

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Observation c17542ac-93b7-483a-a9f0-aeee8928cd75 · outbound

This paper cites Privacy Distillation: Reducing Re-identification Risk of Multimodal Diffusion Models.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Privacy Distillation: Reducing Re-identification Risk of Multimodal Diffusion Models

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Observation c1bfc092-d471-4a67-9ec8-e5ecd059db9b · outbound

This paper cites Privacy-preserving data publishing: A survey of recent developments.ACM Computing Surveys (Csur), 42(4):1–53, 2010.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Privacy-preserving data publishing: A survey of recent developments.ACM Computing Surveys (Csur), 42(4):1–53, 2010

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Observation 10ba2729-41dd-4fb9-96dc-8fb906e13970 · outbound

This paper cites An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion

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Observation e1a3ff5b-3085-40d1-8e5c-747d20438888 · outbound

This paper cites Inverting gradients-how easy is it to break privacy in federated learning?Advances in neural information processing systems, 33:16937– 16947, 2020.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Inverting gradients-how easy is it to break privacy in federated learning?Advances in neural information processing systems, 33:16937– 16947, 2020

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Observation 967ed56c-b20c-4d22-ae24-797eacadcda3 · outbound

This paper cites Learning generative models with sinkhorn divergences.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Learning generative models with sinkhorn divergences

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Observation b3feecb2-1cac-431f-973f-edae722a5dfb · outbound

This paper cites Differentially Private Diffusion Models Generate Useful Synthetic Images.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Differentially Private Diffusion Models Generate Useful Synthetic Images

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Observation 48c03686-1bed-44ae-92e2-fac781172f1e · outbound

This paper cites Generative adversar- ial nets.Advances in neural information processing systems, 27, 2014.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Generative adversar- ial nets.Advances in neural information processing systems, 27, 2014

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Observation 634f262d-2db2-48f5-ae75-5a3cf7015649 · outbound

This paper cites Evaluating deep neural networks trained on clinical images in dermatology with the fitzpatrick 17k dataset.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Evaluating deep neural networks trained on clinical images in dermatology with the fitzpatrick 17k dataset

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Observation 33f95a83-fea2-427f-bc46-2c2bf0bd347e · outbound

This paper cites Dp-merf: Differentially private mean em- beddings with randomfeatures for practical privacy- preserving data generation.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Dp-merf: Differentially private mean em- beddings with randomfeatures for practical privacy- preserving data generation

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Observation 65501f7e-58a8-4199-80d7-1e8b54f463ef · outbound

This paper cites LOGAN: Membership Inference Attacks Against Generative Models.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation LOGAN: Membership Inference Attacks Against Generative Models

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Observation f12becd5-2a12-452d-b2f9-b7b5de9ce869 · outbound

This paper cites Is synthetic data from generative models ready for image recognition?.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Is synthetic data from generative models ready for image recognition?

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Observation f72bed62-8368-4e17-974a-9e970b687e99 · outbound

This paper cites Prompt-to-Prompt Image Editing with Cross Attention Control.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Prompt-to-Prompt Image Editing with Cross Attention Control

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Observation 8627ef07-3c22-4b09-a4e2-c5276392475a · outbound

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

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Gans trained by a two time-scale update rule converge to a local nash equilibrium.Advances in neural information processing systems, 30, 2017

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Observation 6159bb15-a7d5-412b-8763-9736b37d1a36 · outbound

This paper cites Monte carlo and reconstruction membership inference attacks against generative models.Proceed- ings on Privacy Enhancing Technologies, 2019.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Monte carlo and reconstruction membership inference attacks against generative models.Proceed- ings on Privacy Enhancing Technologies, 2019

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Observation dc85379e-55b3-46a9-806e-d7f72b274f33 · outbound

This paper cites Denoising diffusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Denoising diffusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020

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Observation 5636b208-b325-4765-abe8-139ec78802a8 · outbound

This paper cites Cas- caded diffusion models for high fidelity image genera- tion.Journal of Machine Learning Research, 23(47):1– 33, 2022.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Cas- caded diffusion models for high fidelity image genera- tion.Journal of Machine Learning Research, 23(47):1– 33, 2022

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Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:16.949811Z

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Observation e5c3feac-8703-48d4-9295-29bd01027d06 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation LoRA: Low-Rank Adaptation of Large Language Models

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Observation 33928757-0589-4229-8ae9-183735a84695 · outbound

This paper cites Membership Inference of Diffusion Models.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Membership Inference of Diffusion Models

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Observation 5ea591d7-db76-4f1a-9af1-f799e3f69d09 · outbound

This paper cites Membership in- ference attacks on machine learning: A survey.ACM Computing Surveys (CSUR), 54(11s):1–37, 2022.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Membership in- ference attacks on machine learning: A survey.ACM Computing Surveys (CSUR), 54(11s):1–37, 2022

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Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:16.961963Z

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Observation 6f94c15a-d883-4363-a868-ef022959faa0 · outbound

This paper cites Sok: Privacy- preserving data synthesis.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Sok: Privacy- preserving data synthesis

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Observation 6318102a-78d7-452e-8dfd-fcfa943eca7f · outbound

This paper cites Chexpert: A large chest radiograph dataset with uncer- tainty labels and expert comparison.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Chexpert: A large chest radiograph dataset with uncer- tainty labels and expert comparison

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Observation c7b047fd-8bb7-41ff-b60f-c4287659404b · outbound

This paper cites Provable Membership Inference Privacy.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Provable Membership Inference Privacy

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Resolution: verified exact; local_arxiv, observed 2026-08-06T23:12:17.682873Z

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Observation 57e712db-6805-410f-9266-bd6321dbf6bb · outbound

This paper cites MIAShield: Defending Membership Inference Attacks via Preemptive Exclusion of Members.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation MIAShield: Defending Membership Inference Attacks via Preemptive Exclusion of Members

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Resolution: verified exact; local_arxiv, observed 2026-08-06T23:12:17.668279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 40d8bc9b-4522-42f2-b3db-07877afe8403 · outbound

This paper cites Evaluating differ- entially private machine learning in practice.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Evaluating differ- entially private machine learning in practice

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:16.980789Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93a479c3-702d-4e92-82b7-34dbe04754ba · outbound

This paper cites DP$^2$-VAE: Differentially Private Pre-trained Variational Autoencoders.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation DP$^2$-VAE: Differentially Private Pre-trained Variational Autoencoders

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:12:17.655084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b3165eef-b6a3-47de-8599-9fe896db296d · outbound

This paper cites Progressive Growing of GANs for Improved Quality, Stability, and Variation.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Progressive Growing of GANs for Improved Quality, Stability, and Variation

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:16.989491Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73e59ad3-4e6a-430b-8e62-077532cab71a · outbound

This paper cites Training gener- ative adversarial networks with limited data.Advances in neural information processing systems, 33:12104– 12114, 2020.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Training gener- ative adversarial networks with limited data.Advances in neural information processing systems, 33:12104– 12114, 2020

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:16.993196Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d9266805-238d-4425-abf1-ac0e49b842f4 · outbound

This paper cites Analyzing and improving the image quality of stylegan.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Analyzing and improving the image quality of stylegan

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:16.996933Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ffd792fa-b6b5-418a-a641-bca9812144f0 · outbound

This paper cites Imagic: Text-based real image editing with dif- fusion models.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Imagic: Text-based real image editing with dif- fusion models

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:17.000365Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5673020f-4f97-494b-81f1-0d58834d718c · outbound

This paper cites When does data augmentation help with membership inference attacks? InInternational conference on machine learn- ing, pages 5345–5355.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation When does data augmentation help with membership inference attacks? InInternational conference on machine learn- ing, pages 5345–5355

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:17.003702Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce1ef253-97be-41fb-812d-3865cff5573c · outbound

This paper cites Privacy-preserving artificial intelligence in healthcare: Techniques and applications.Computers in Biology and Medicine, 158:106848, 2023.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Privacy-preserving artificial intelligence in healthcare: Techniques and applications.Computers in Biology and Medicine, 158:106848, 2023

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:17.008041Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f5bd0d0a-f62c-4429-a7da-9aa45dea1a10 · outbound

This paper cites Auto-Encoding Variational Bayes.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Auto-Encoding Variational Bayes

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:17.011390Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3abfbd5-4183-493e-8247-a0b88cd088ad · outbound

This paper cites An ef- ficient membership inference attack for the diffusion model by proximal initialization.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation An ef- ficient membership inference attack for the diffusion model by proximal initialization

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:17.015902Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d42b7a00-8343-4d73-bcc8-ad6bdaaf6793 · outbound

This paper cites Deep learning for medical image cryptography: A compre- hensive review.Applied Sciences, 13(14):8295, 2023.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Deep learning for medical image cryptography: A compre- hensive review.Applied Sciences, 13(14):8295, 2023

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:17.019799Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9a497479-020c-4580-874a-3b15c8bf33b7 · outbound

This paper cites SynthEval: A Framework for Detailed Utility and Privacy Evaluation of Tabular Synthetic Data.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation SynthEval: A Framework for Detailed Utility and Privacy Evaluation of Tabular Synthetic Data

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:17.023902Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation abe8d60c-347b-40cb-85a9-c37943981b42 · outbound

This paper cites Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language mod- els.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language mod- els

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:17.028306Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f428ed45-81b2-49c2-a92d-45f751f24bb2 · outbound

This paper cites Exploring the benefits of visual prompting in differential privacy.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Exploring the benefits of visual prompting in differential privacy

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:17.032230Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d56859bf-808f-4ee0-a436-4cadf82880c9 · outbound

This paper cites Deep learning face attributes in the wild.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Deep learning face attributes in the wild

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:17.036621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6e9a421-3dda-4699-8210-eb4a7e51535d · outbound

This paper cites G- pate: Scalable differentially private data generator via private aggregation of teacher discriminators.Ad- vances in Neural Information Processing Systems, 34:2965–2977, 2021.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation G- pate: Scalable differentially private data generator via private aggregation of teacher discriminators.Ad- vances in Neural Information Processing Systems, 34:2965–2977, 2021

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:17.040552Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bca28483-3037-4911-a24b-4c2487a04c72 · outbound

This paper cites Repaint: Inpainting using denoising diffusion probabilistic mod- els, 2022.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Repaint: Inpainting using denoising diffusion probabilistic mod- els, 2022

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:17.044026Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29acdc6f-f0c8-410a-8a97-c7c45af4c3fb · outbound

This paper cites Privacy-Preserving Low-Rank Adaptation against Membership Inference Attacks for Latent Diffusion Models.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Privacy-Preserving Low-Rank Adaptation against Membership Inference Attacks for Latent Diffusion Models

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:17.048041Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1b2cf05-69c3-4cc1-aee1-c23a2fad6ae0 · outbound

This paper cites DP-LDMs: Differentially Private Latent Diffusion Models.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation DP-LDMs: Differentially Private Latent Diffusion Models

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:17.051778Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53da0ee9-8249-4e99-8c75-0b481fb4e6ab · outbound

This paper cites Membership inference attacks against diffusion mod- els.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Membership inference attacks against diffusion mod- els

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:17.055522Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0533e0bc-b693-4cae-bc05-fa74db5245ad · outbound

This paper cites Exploiting unintended feature leakage in collaborative learning.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Exploiting unintended feature leakage in collaborative learning

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:17.059658Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0bbc068-416a-4eeb-b259-9104b793f2b4 · outbound

This paper cites SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:17.063266Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c244a090-dbdb-4cf5-8a7a-321be2b7788b · outbound

This paper cites Conditional Generative Adversarial Nets.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Conditional Generative Adversarial Nets

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:17.066474Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13ac35a3-8d1e-4373-a470-58d33906cb12 · outbound

This paper cites Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANs.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANs

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:17.070094Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce7a5d85-41d4-4035-9bb6-ad054e2e3179 · outbound

This paper cites Null-text inversion for editing real images using guided diffusion models.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Null-text inversion for editing real images using guided diffusion models

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:17.074127Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27dbb143-1b82-4a0e-b24d-baabcae95729 · outbound

This paper cites Ma- chine learning with membership privacy using adver- sarial regularization.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Ma- chine learning with membership privacy using adver- sarial regularization

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:17.077800Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69b9664a-0307-4658-b4f3-7af5751bc953 · outbound

This paper cites Com- prehensive privacy analysis of deep learning: Passive and active white-box inference attacks against central- ized and federated learning.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Com- prehensive privacy analysis of deep learning: Passive and active white-box inference attacks against central- ized and federated learning

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:17.081544Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18fc79ed-0299-454a-92d0-e893d5307dd4 · outbound

This paper cites Dataset Meta-Learning from Kernel Ridge-Regression.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Dataset Meta-Learning from Kernel Ridge-Regression

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:17.085681Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b13e5a4b-e371-4598-9dd6-0cd97c4e7c94 · outbound

This paper cites Dataset distillation with infinitely wide convolutional networks.Advances in Neural Informa- tion Processing Systems, 34:5186–5198, 2021.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Dataset distillation with infinitely wide convolutional networks.Advances in Neural Informa- tion Processing Systems, 34:5186–5198, 2021

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:17.089964Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f3eda1a6-7b2b-45d0-9a97-8e9a1cc912e9 · outbound

This paper cites SoK: Comparing Different Membership Inference Attacks with a Comprehensive Benchmark.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation SoK: Comparing Different Membership Inference Attacks with a Comprehensive Benchmark

Reference 84

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:12:17.552610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 495c693b-4cd1-44c2-94b1-602aa56a90a8 · outbound

This paper cites Black-box Membership Inference Attacks against Fine-tuned Diffusion Models.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Black-box Membership Inference Attacks against Fine-tuned Diffusion Models

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:17.097590Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0aa3d19-33ab-472c-889b-9bc74835f582 · outbound

This paper cites White-box Membership Inference Attacks against Diffusion Models.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation White-box Membership Inference Attacks against Diffusion Models

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:17.101974Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7611d84e-c644-41fc-a50f-ab0eeb7fee5b · outbound

This paper cites Scalable Private Learning with PATE.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Scalable Private Learning with PATE

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:17.105614Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3bd8337-36f6-4b9d-b526-b00c6dc2aaf9 · outbound

This paper cites Sinkhorn autoencoders.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Sinkhorn autoencoders

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:17.109853Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c7697e6-0244-4414-8ca5-6fbb06ea59dd · outbound

This paper cites Synthcity: facilitating innovative use cases of synthetic data in different data modalities.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Synthcity: facilitating innovative use cases of synthetic data in different data modalities

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:17.113859Z

Source-reported events for the cited work

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Observation fa5fb96c-edcc-4793-b534-629998228795 · outbound

This paper cites Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:17.118096Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae4fcf72-18ac-4e8d-8c8a-9d147e42b3af · outbound

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

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:17.121817Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1373eecc-ced8-49d3-99eb-269eea47a611 · outbound

This paper cites Generating diverse high-fidelity images with vq-vae-2.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Generating diverse high-fidelity images with vq-vae-2

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:17.125208Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation beace0a0-8e3a-4e10-b789-f74732d2c372 · outbound

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

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation High-resolution im- age synthesis with latent diffusion models

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:17.129237Z

Source-reported events for the cited work

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Observation 49567043-a131-49ee-9886-77115d25949c · outbound

This paper cites Dream- booth: Fine tuning text-to-image diffusion models for subject-driven generation.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Dream- booth: Fine tuning text-to-image diffusion models for subject-driven generation

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:17.132518Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97972cef-adae-4d1b-94fd-c0fd9090ebc3 · outbound

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

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Photorealistic text-to-image diffusion models with deep language understanding.Advances in neural information processing systems, 35:36479– 36494, 2022

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:18.111428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6024980a-1fac-4b5b-8ea9-c6e3b63e3dd7 · outbound

This paper cites ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:17.140839Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1509a925-dc97-45dc-887a-21f2c0e0f884 · outbound

This paper cites Improving GANs Using Optimal Transport.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Improving GANs Using Optimal Transport

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:17.145012Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c890f26-eb7e-46a0-9187-3a81824672dc · outbound

This paper cites Synthetic Data: Revisiting the Privacy-Utility Trade-off.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Synthetic Data: Revisiting the Privacy-Utility Trade-off

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:17.148866Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1151e7fe-cd7e-40fa-a798-73df179fa20b · outbound

This paper cites Dragdiffusion: Harnessing diffusion models for interactive point-based image editing.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Dragdiffusion: Harnessing diffusion models for interactive point-based image editing

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:18.099537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ff23c053-8f0a-4d76-86e1-89e55f7fb45a · outbound

This paper cites Diversity is definitely needed: Improving model-agnostic zero-shot classi- fication via stable diffusion.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Diversity is definitely needed: Improving model-agnostic zero-shot classi- fication via stable diffusion

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:18.089854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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