SOTAVerified

Image Generation

Image Generation (synthesis) is the task of generating new images from an existing dataset.

  • Unconditional generation refers to generating samples unconditionally from the dataset, i.e. $p(y)$
  • Conditional image generation (subtask) refers to generating samples conditionally from the dataset, based on a label, i.e. $p(y|x)$.

In this section, you can find state-of-the-art leaderboards for unconditional generation. For conditional generation, and other types of image generations, refer to the subtasks.

( Image credit: StyleGAN )

Papers

Showing 41514175 of 6689 papers

TitleStatusHype
A Survey on Adversarial Image Synthesis0
Towards More Accurate Fake Detection on Images Generated from Advanced Generative and Neural Rendering Models0
Quasi-Taylor Samplers for Diffusion Generative Models based on Ideal Derivatives0
It's a Feature, Not a Bug: Measuring Creative Fluidity in Image Generators0
Adversarial Rain Attack and Defensive Deraining for DNN Perception0
IVE-GAN: Invariant Encoding Generative Adversarial Networks0
iWarpGAN: Disentangling Identity and Style to Generate Synthetic Iris Images0
Jailbreaking Prompt Attack: A Controllable Adversarial Attack against Diffusion Models0
A Survey of Representation Learning, Optimization Strategies, and Applications for Omnidirectional Vision0
Jean-Luc Picard at Touché 2023: Comparing Image Generation, Stance Detection and Feature Matching for Image Retrieval for Arguments0
JeDi: Joint-Image Diffusion Models for Finetuning-Free Personalized Text-to-Image Generation0
JetFormer: An Autoregressive Generative Model of Raw Images and Text0
JGAN: A Joint Formulation of GAN for Synthesizing Images and Labels0
Joint Color-Spatial-Directional clustering and Region Merging (JCSD-RM) for unsupervised RGB-D image segmentation0
A Survey of Generative AI for Intelligent Transportation Systems: Road Transportation Perspective0
JointDiT: Enhancing RGB-Depth Joint Modeling with Diffusion Transformers0
Joint Generative Modeling of Scene Graphs and Images via Diffusion Models0
Joint haze image synthesis and dehazing with mmd-vae losses0
Jointly Adversarial Network to Wavelength Compensation and Dehazing of Underwater Images0
Jointly Trained Image and Video Generation using Residual Vectors0
Jointly Training Large Autoregressive Multimodal Models0
JointNet: Extending Text-to-Image Diffusion for Dense Distribution Modeling0
Joint Pose and Expression Modeling for Facial Expression Recognition0
A^3DSegNet: Anatomy-aware artifact disentanglement and segmentation network for unpaired segmentation, artifact reduction, and modality translation0
JoyType: A Robust Design for Multilingual Visual Text Creation0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Improved DDPMFID12.3Unverified
2ADMFID11.84Unverified
3BigGAN-deepFID8.1Unverified
4Polarity-BigGANFID6.82Unverified
5VQGAN+Transformer (k=mixed, p=1.0, a=0.005)FID6.59Unverified
6MaskGITFID6.18Unverified
7VQGAN+Transformer (k=600, p=1.0, a=0.05)FID5.2Unverified
8CDMFID4.88Unverified
9ADM-GFID4.59Unverified
10RINFID4.51Unverified
#ModelMetricClaimedVerifiedStatus
1PresGANFID52.2Unverified
2RESFLOWFID48.29Unverified
3Residual FlowFID46.37Unverified
4GLF+perceptual loss (ours)FID44.6Unverified
5ProdPoly no activation functionsFID40.45Unverified
6ProdPoly no activation functionsFID36.77Unverified
7ACGANFID35.47Unverified
8DenseFlow-74-10FID34.9Unverified
9NVAE w/ flowFID32.53Unverified
10QSNGANFID31.97Unverified
#ModelMetricClaimedVerifiedStatus
1GLIDE + CLSFID30.87Unverified
2GLIDE + CLIPFID30.46Unverified
3GLIDE + CLS-FREEFID29.22Unverified
4GLIDE + CLIP + CLS + CLS-FREEFID29.18Unverified
5PGMGANFID21.73Unverified
6CLR-GANFID20.27Unverified
7FMFID14.45Unverified
8CT (Direct Generation, NFE=1)FID13Unverified
9CT (Direct Generation, NFE=2)FID11.1Unverified
10GLIDE +CLSKID7.95Unverified