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 17261750 of 6689 papers

TitleStatusHype
Unsupervised Image Generation with Infinite Generative Adversarial NetworksCode1
Orthogonal Jacobian Regularization for Unsupervised Disentanglement in Image GenerationCode1
Contextual Convolutional Neural NetworksCode1
ILVR: Conditioning Method for Denoising Diffusion Probabilistic ModelsCode1
Sketch Your Own GANCode1
Toward Spatially Unbiased Generative ModelsCode1
Continuous Language Generative FlowCode1
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
Audio2Head: Audio-driven One-shot Talking-head Generation with Natural Head MotionCode1
ViTGAN: Training GANs with Vision TransformersCode1
Bi-level Feature Alignment for Versatile Image Translation and ManipulationCode1
CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series ImputationCode1
DocSynth: A Layout Guided Approach for Controllable Document Image SynthesisCode1
Improving Text-to-Image Synthesis Using Contrastive LearningCode1
Variational Diffusion ModelsCode1
Explainable Diabetic Retinopathy Detection and Retinal Image GenerationCode1
ResViT: Residual vision transformers for multi-modal medical image synthesisCode1
SITTA: Single Image Texture Translation for Data AugmentationCode1
NP-DRAW: A Non-Parametric Structured Latent Variable Model for Image GenerationCode1
Fairness for Image Generation with Uncertain Sensitive AttributesCode1
Total Generate: Cycle in Cycle Generative Adversarial Networks for Generating Human Faces, Hands, Bodies, and Natural ScenesCode1
Manifold Matching via Deep Metric Learning for Generative ModelingCode1
Adaptive Convolutions for Structure-Aware Style TransferCode1
Self-Supervised GANs with Label AugmentationCode1
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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