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 20762100 of 6689 papers

TitleStatusHype
Self-Attention Generative Adversarial NetworksCode1
Image Generation from Scene GraphsCode1
Cross-View Image Synthesis using Conditional GANsCode1
Adversarial Audio SynthesisCode1
PixelSNAIL: An Improved Autoregressive Generative ModelCode1
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANsCode1
Deep Image PriorCode1
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial NetworksCode1
StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial NetworksCode1
NiftyNet: a deep-learning platform for medical imagingCode1
Semantic Image Synthesis via Adversarial LearningCode1
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash EquilibriumCode1
Sliced Wasserstein Generative ModelsCode1
How to Make an Image More Memorable? A Deep Style Transfer ApproachCode1
Improved Training of Wasserstein GANsCode1
CVAE-GAN: Fine-Grained Image Generation through Asymmetric TrainingCode1
Wasserstein GANCode1
PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other ModificationsCode1
A General and Adaptive Robust Loss FunctionCode1
Fast Patch-based Style Transfer of Arbitrary StyleCode1
Conditional Image Synthesis With Auxiliary Classifier GANsCode1
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial NetsCode1
Improved Techniques for Training GANsCode1
Incorporating long-range consistency in CNN-based texture generationCode1
Density estimation using Real NVPCode1
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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