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 5151–5175 of 6689 papers

TitleStatusHype
Unconditional Scene Graph Generation—0
Learning to Rearrange Voxels in Binary Segmentation Masks for Smooth Manifold TriangulationCode0
Interpreting Generative Adversarial Networks for Interactive Image Generation—0
BIGRoC: Boosting Image Generation via a Robust ClassifierCode0
ILVR: Conditioning Method for Denoising Diffusion Probabilistic ModelsCode1
Sketch Your Own GANCode1
Combining Attention with Flow for Person Image Synthesis—0
Neural Scene Decoration from a Single PhotographCode0
Toward Spatially Unbiased Generative ModelsCode1
Image Augmentation Using a Task Guided Generative Adversarial Network for Age Estimation on Brain MRICode0
Cycle-Consistent Inverse GAN for Text-to-Image Synthesis—0
Categorical EHR Imputation with Generative Adversarial Nets—0
Training face verification models from generated face identity data—0
SDEdit: Guided Image Synthesis and Editing with Stochastic Differential EquationsCode2
Continuous Language Generative FlowCode1
CRD-CGAN: Category-Consistent and Relativistic Constraints for Diverse Text-to-Image Generation—0
Realistic Ultrasound Image Synthesis for Improved Classification of Liver Disease—0
DCL: Differential Contrastive Learning for Geometry-Aware Depth SynthesisCode0
Black-Box Diagnosis and Calibration on GAN Intra-Mode Collapse: A Pilot StudyCode0
RewriteNet: Reliable Scene Text Editing with Implicit Decomposition of Text Contents and StylesCode0
3D-StyleGAN: A Style-Based Generative Adversarial Network for Generative Modeling of Three-Dimensional Medical ImagesCode1
SynthTIGER: Synthetic Text Image GEneratoR Towards Better Text Recognition ModelsCode1
Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging—0
Audio2Head: Audio-driven One-shot Talking-head Generation with Natural Head MotionCode1
Frequency-Supervised MR-to-CT Image SynthesisCode0
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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