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

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Showing 3326–3350 of 6689 papers

TitleStatusHype
Traditional Classification Neural Networks are Good Generators: They are Competitive with DDPMs and GANs—0
Learning Universal Policies via Text-Guided Video Generation—0
Learning Unnormalized Statistical Models via Compositional Optimization—0
Learning Versatile 3D Shape Generation with Improved AR Models—0
Learning Versatile 3D Shape Generation with Improved Auto-regressive Models—0
Learning What and Where to Draw—0
LEDiff: Latent Exposure Diffusion for HDR Generation—0
LEDITS: Real Image Editing with DDPM Inversion and Semantic Guidance—0
Lesion Conditional Image Generation for Improved Segmentation of Intracranial Hemorrhage from CT Images—0
Less is More: Unsupervised Mask-guided Annotated CT Image Synthesis with Minimum Manual Segmentations—0
Weakly Supervised Annotations for Multi-modal Greeting Cards Dataset—0
A Self-attention Guided Multi-scale Gradient GAN for Diversified X-ray Image Synthesis—0
Let's Go Shopping (LGS) -- Web-Scale Image-Text Dataset for Visual Concept Understanding—0
Let's Verify and Reinforce Image Generation Step by Step—0
Let's ViCE! Mimicking Human Cognitive Behavior in Image Generation Evaluation—0
Density-aware Haze Image Synthesis by Self-Supervised Content-Style Disentanglement—0
Leveraging Generative AI Models to Explore Human Identity—0
Weakly Supervised Keypoint Discovery—0
Leveraging Previous Steps: A Training-free Fast Solver for Flow Diffusion—0
Weakly-Supervised Photo-realistic Texture Generation for 3D Face Reconstruction—0
Leveraging Semantic Attribute Binding for Free-Lunch Color Control in Diffusion Models—0
Leveraging Text-to-Image Generation for Handling Spurious Correlation—0
Leveraging Unpaired Data for Vision-Language Generative Models via Cycle Consistency—0
Leveraging Vision-Language Foundation Models to Reveal Hidden Image-Attribute Relationships in Medical Imaging—0
Leveraging Visual Question Answering to Improve Text-to-Image Synthesis—0
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All datasetsImageNet 256x256CIFAR-10ImageNet 64x64ImageNet 512x512FFHQ 256 x 256CelebA 64x64ImageNet 32x32LSUN Bedroom 256 x 256STL-10LSUN Churches 256 x 256ImageNet 128x128FFHQ 1024 x 1024

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Improved DDPMFID12.3—Unverified
2ADMFID11.84—Unverified
3BigGAN-deepFID8.1—Unverified
4Polarity-BigGANFID6.82—Unverified
5VQGAN+Transformer (k=mixed, p=1.0, a=0.005)FID6.59—Unverified
6MaskGITFID6.18—Unverified
7VQGAN+Transformer (k=600, p=1.0, a=0.05)FID5.2—Unverified
8CDMFID4.88—Unverified
9ADM-GFID4.59—Unverified
10RINFID4.51—Unverified
#ModelMetricClaimedVerifiedStatus
1PresGANFID52.2—Unverified
2RESFLOWFID48.29—Unverified
3Residual FlowFID46.37—Unverified
4GLF+perceptual loss (ours)FID44.6—Unverified
5ProdPoly no activation functionsFID40.45—Unverified
6ProdPoly no activation functionsFID36.77—Unverified
7ACGANFID35.47—Unverified
8DenseFlow-74-10FID34.9—Unverified
9NVAE w/ flowFID32.53—Unverified
10QSNGANFID31.97—Unverified
#ModelMetricClaimedVerifiedStatus
1GLIDE + CLSFID30.87—Unverified
2GLIDE + CLIPFID30.46—Unverified
3GLIDE + CLS-FREEFID29.22—Unverified
4GLIDE + CLIP + CLS + CLS-FREEFID29.18—Unverified
5PGMGANFID21.73—Unverified
6CLR-GANFID20.27—Unverified
7FMFID14.45—Unverified
8CT (Direct Generation, NFE=1)FID13—Unverified
9CT (Direct Generation, NFE=2)FID11.1—Unverified
10GLIDE +CLSKID7.95—Unverified