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 5176–5200 of 6689 papers

TitleStatusHype
DE-FAKE: Detection and Attribution of Fake Images Generated by Text-to-Image Generation Models—0
DefectFill: Realistic Defect Generation with Inpainting Diffusion Model for Visual Inspection—0
Defect Image Sample Generation With Diffusion Prior for Steel Surface Defect Recognition—0
Defect Transfer GAN: Diverse Defect Synthesis for Data Augmentation—0
Can Generative AI Replace Immunofluorescent Staining Processes? A Comparison Study of Synthetically Generated CellPainting Images from Brightfield—0
Defining and Quantifying Creative Behavior in Popular Image Generators—0
Deformation-aware GAN for Medical Image Synthesis with Substantially Misaligned Pairs—0
Neural Language of Thought Models—0
DEGAS: Differentiable Efficient Generator Search—0
Dehazing-NeRF: Neural Radiance Fields from Hazy Images—0
Deli-Fisher GAN: Stable and Efficient Image Generation With Structured Latent Generative Space—0
Structure-Guided Adversarial Training of Diffusion Models—0
VirtualModel: Generating Object-ID-retentive Human-object Interaction Image by Diffusion Model for E-commerce Marketing—0
Delving into Rectifiers in Style-Based Image Translation—0
Can CLIP Count Stars? An Empirical Study on Quantity Bias in CLIP—0
Can Artificial Intelligence Reconstruct Ancient Mosaics?—0
Denoising Autoregressive Representation Learning—0
Structure-Transformed Texture-Enhanced Network for Person Image Synthesis—0
Causal Adversarial Network for Learning Conditional and Interventional Distributions—0
CAMPARI: Camera-Aware Decomposed Generative Neural Radiance Fields—0
Student's t-Generative Adversarial Networks—0
Camouflaged Image Synthesis Is All You Need to Boost Camouflaged Detection—0
Denoising Diffusion Probabilistic Models for Image Inpainting of Cell Distributions in the Human Brain—0
StyLandGAN: A StyleGAN based Landscape Image Synthesis using Depth-map—0
StyleAdapter: A Unified Stylized Image Generation Model—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