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 6351–6375 of 6689 papers

TitleStatusHype
Flow-based Network Traffic Generation using Generative Adversarial Networks—0
Unsupervised Person Image Synthesis in Arbitrary Poses—0
Vector Learning for Cross Domain Representations—0
Semantically Invariant Text-to-Image Generation—0
SAR TO OPTICAL IMAGE SYNTHESIS FOR CLOUD REMOVAL WITH GENERATIVE ADVERSARIAL NETWORKSCode0
Learning to Detect Fake Face Images in the Wild—0
C4Synth: Cross-Caption Cycle-Consistent Text-to-Image Synthesis—0
Pose Estimation for Non-Cooperative Spacecraft Rendezvous Using Convolutional Neural Networks—0
Feature2Mass: Visual Feature Processing in Latent Space for Realistic Labeled Mass Generation—0
Geometric Image Synthesis—0
Unpaired Brain MR-to-CT Synthesis using a Structure-Constrained CycleGAN—0
Neural Rendering and Reenactment of Human Actor Videos—0
Mapping Instructions to Actions in 3D Environments with Visual Goal PredictionCode0
Open Set Learning with Counterfactual Images—0
On Learning 3D Face Morphable Model from In-the-wild ImagesCode0
Escaping from Collapsing Modes in a Constrained SpaceCode0
TreeGAN: Syntax-Aware Sequence Generation with Generative Adversarial Networks—0
Learning Hierarchical Semantic Image Manipulation through Structured RepresentationsCode0
Text-to-image Synthesis via Symmetrical Distillation Networks—0
GridFace: Face Rectification via Learning Local Homography Transformations—0
Cross-view image synthesis using geometry-guided conditional GANsCode1
Towards Audio to Scene Image Synthesis using Generative Adversarial Network—0
Unsupervised learning for cross-domain medical image synthesis using deformation invariant cycle consistency networks—0
Paired 3D Model Generation with Conditional Generative Adversarial NetworksCode0
Diverse Conditional Image Generation by Stochastic Regression with Latent Drop-Out CodesCode0
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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