GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification | Paper Citation Record

Observation c87bfb71-9fcb-4b3f-97ff-76f1aa3c93b5 · outbound

This paper cites Improving image generation with better captions.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification Improving image generation with better captions

Reference 1

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Observation 680d233d-424c-4daf-b1f6-feef0635d7ba · outbound

This paper cites CIFAKE: Image classification and explainable identification of AI-generated synthetic images.IEEE Access, 12:15642–15650, 2024.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification CIFAKE: Image classification and explainable identification of AI-generated synthetic images.IEEE Access, 12:15642–15650, 2024

Reference 2

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This paper cites FLUX.1.https://github.com/black-forest-labs/flux, 2024.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification FLUX.1.https://github.com/black-forest-labs/flux, 2024

Reference 3

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This paper cites FLUX.2: Frontier Visual Intelligence.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification FLUX.2: Frontier Visual Intelligence

Reference 4

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This paper cites HunyuanImage 3.0 Technical Report.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification HunyuanImage 3.0 Technical Report

Reference 5

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This paper cites CO-SPY: Com- bining semantic and pixel features to detect synthetic images by AI.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification CO-SPY: Com- bining semantic and pixel features to detect synthetic images by AI

Reference 6

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This paper cites Pytorch cifar models.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification Pytorch cifar models

Reference 7

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This paper cites Angelopoulos, Tianle Li, Dacheng Li, Hao Zhang, Banghua Zhu, Michael I.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification Angelopoulos, Tianle Li, Dacheng Li, Hao Zhang, Banghua Zhu, Michael I

Reference 8

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This paper cites On the detection of synthetic images generated by diffusion models.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification On the detection of synthetic images generated by diffusion models

Reference 9

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Observation c07bf614-89f5-4c7c-b0d8-68981ba22da7 · outbound

This paper cites Raising the Bar of AI-generated Image Detection with CLIP.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification Raising the Bar of AI-generated Image Detection with CLIP

Reference 10

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Observation 4faa78be-eb4d-465a-b8b4-3ec7458151ff · outbound

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

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification ImageNet: A large-scale hierarchical image database

Reference 11

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Observation b896057b-ea9a-466d-b7f7-05a1932e7dc9 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification An image is worth 16x16 words: Transformers for image recognition at scale

Reference 12

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Observation 558031bb-29c4-4474-8e9c-a5827b4f7eb1 · outbound

This paper cites Scaling Rectified Flow Transformers for High-Resolution Image Synthesis.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification Scaling Rectified Flow Transformers for High-Resolution Image Synthesis

Reference 13

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Observation 0e951a22-466e-436a-82b0-8af2dbd1ad2b · outbound

This paper cites Leveraging frequency analysis for deep fake image recognition.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification Leveraging frequency analysis for deep fake image recognition

Reference 14

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Observation a76fbe61-3622-44ae-bcbe-871dd9f441f6 · outbound

This paper cites Generative adversarial nets.Advances in Neural Information Processing Systems (NeurIPS), 27, 2014.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification Generative adversarial nets.Advances in Neural Information Processing Systems (NeurIPS), 27, 2014

Reference 15

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This paper cites Are GAN generated images easy to detect? A critical analysis of the state-of-the-art.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification Are GAN generated images easy to detect? A critical analysis of the state-of-the-art

Reference 16

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Observation ae52179b-22e6-4cd5-9dec-1c0d59e260f4 · outbound

This paper cites Deep residual learning for image recognition.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification Deep residual learning for image recognition

Reference 17

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

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification GANs trained by a two time-scale update rule converge to a local Nash equilibrium

Reference 18

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Observation 02eb5c21-7229-4a4b-b4a0-b8e8099fffff · outbound

This paper cites Denoising Diffusion Probabilistic Models.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification Denoising Diffusion Probabilistic Models

Reference 19

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Observation 8ee6a28f-4980-4fe8-a8ad-dbd695bd934f · outbound

This paper cites Wild- Fake: A large-scale and hierarchical dataset for AI-generated images detection.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification Wild- Fake: A large-scale and hierarchical dataset for AI-generated images detection

Reference 20

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Observation f1a2ebf3-1ccf-4391-8ed6-23246b0fa909 · outbound

This paper cites Densely connected convolutional networks.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification Densely connected convolutional networks

Reference 21

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This paper cites Progressive growing of GANs for improved quality, stability, and variation.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification Progressive growing of GANs for improved quality, stability, and variation

Reference 22

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Observation 15f387b3-1090-45c9-8c03-49f937df77a7 · outbound

This paper cites A Style-Based Generator Architecture for Generative Adversarial Networks.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification A Style-Based Generator Architecture for Generative Adversarial Networks

Reference 23

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This paper cites Learning multiple layers of features from tiny images.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification Learning multiple layers of features from tiny images

Reference 24

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This paper cites Improved Precision and Recall Metric for Assessing Generative Models.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification Improved Precision and Recall Metric for Assessing Generative Models

Reference 25

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Observation 719d2f42-351e-4b3a-b939-f0ba949d7342 · outbound

This paper cites Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

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This paper cites Swin-base fine-tuned on cifar-10.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification Swin-base fine-tuned on cifar-10

Reference 27

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This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification Swin transformer: Hierarchical vision transformer using shifted windows

Reference 28

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This paper cites A ConvNet for the 2020s.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification A ConvNet for the 2020s

Reference 29

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This paper cites Decoupled Weight Decay Regularization.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification Decoupled Weight Decay Regularization

Reference 30

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This paper cites When does label smoothing help? In Advances in Neural Information Processing Systems (NeurIPS), pages 4694–4703, 2019.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification When does label smoothing help? In Advances in Neural Information Processing Systems (NeurIPS), pages 4694–4703, 2019

Reference 31

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GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification Unresolved cited work

Reference 32

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This paper cites Towards Universal Fake Image Detectors that Generalize Across Generative Models.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification Towards Universal Fake Image Detectors that Generalize Across Generative Models

Reference 33

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This paper cites Pytorch cifar-10 pre-trained models.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification Pytorch cifar-10 pre-trained models

Reference 34

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This paper cites Qwen-image: Advancing text-to-image generation via latent diffusion.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification Qwen-image: Advancing text-to-image generation via latent diffusion

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This paper cites Designing Network Design Spaces.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification Designing Network Design Spaces

Reference 36

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

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 37

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This paper cites AEROBLADE: Training-Free Detection of Latent Diffusion Images Using Autoencoder Reconstruction Error.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification AEROBLADE: Training-Free Detection of Latent Diffusion Images Using Autoencoder Reconstruction Error

Reference 38

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

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification High- resolution image synthesis with latent diffusion models

Reference 39

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This paper cites Improved techniques for training GANs.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification Improved techniques for training GANs

Reference 40

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This paper cites MobileNetV2: Inverted residuals and linear bottlenecks.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification MobileNetV2: Inverted residuals and linear bottlenecks

Reference 41

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This paper cites DE-FAKE: Detection and Attribution of Fake Images Generated by Text-to-Image Generation Models.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification DE-FAKE: Detection and Attribution of Fake Images Generated by Text-to-Image Generation Models

Reference 42

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This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 43

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This paper cites Stable diffusion 3.5.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification Stable diffusion 3.5

Reference 44

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This paper cites Going deeper with convolutions.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification Going deeper with convolutions

Reference 45

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This paper cites Re- thinking the inception architecture for computer vision.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification Re- thinking the inception architecture for computer vision

Reference 46

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This paper cites MnasNet: Platform-aware neural architecture search for mobile.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification MnasNet: Platform-aware neural architecture search for mobile

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This paper cites EfficientNet: Rethinking model scaling for convolutional neural networks.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification EfficientNet: Rethinking model scaling for convolutional neural networks

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This paper cites EfficientNetV2: Smaller models and faster training.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification EfficientNetV2: Smaller models and faster training

Reference 49

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This paper cites CNN- generated images are surprisingly easy to spot.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification CNN- generated images are surprisingly easy to spot

Reference 50

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This paper cites ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders

Reference 51

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This paper cites Vit-base-patch16-224 fine-tuned on cifar-10.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification Vit-base-patch16-224 fine-tuned on cifar-10

Reference 52

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This paper cites GenImage: A Million-Scale Benchmark for Detecting AI-Generated Image.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification GenImage: A Million-Scale Benchmark for Detecting AI-Generated Image

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