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 19762000 of 6689 papers

TitleStatusHype
Training with Quantization Noise for Extreme Model CompressionCode1
Melanoma Detection using Adversarial Training and Deep Transfer LearningCode1
Melanoma Detection using Adversarial Training and Deep Transfer LearningCode1
Decoupling Global and Local Representations via Invertible Generative FlowsCode1
Cross-domain Correspondence Learning for Exemplar-based Image TranslationCode1
SESAME: Semantic Editing of Scenes by Adding, Manipulating or Erasing ObjectsCode1
Normalizing Flows with Multi-Scale Autoregressive PriorsCode1
Attentive Normalization for Conditional Image GenerationCode1
Semantic Image Manipulation Using Scene GraphsCode1
Multimodal Image Synthesis with Conditional Implicit Maximum Likelihood EstimationCode1
Text-Guided Neural Image InpaintingCode1
Evolving Normalization-Activation LayersCode1
Feature Quantization Improves GAN TrainingCode1
Structural-analogy from a Single Image PairCode1
Bodies at Rest: 3D Human Pose and Shape Estimation from a Pressure Image using Synthetic DataCode1
Edge Guided GANs with Contrastive Learning for Semantic Image SynthesisCode1
Semantically Multi-modal Image SynthesisCode1
Controllable Person Image Synthesis with Attribute-Decomposed GANCode1
Cycle Text-To-Image GAN with BERTCode1
BachGAN: High-Resolution Image Synthesis from Salient Object LayoutCode1
Learning Layout and Style Reconfigurable GANs for Controllable Image SynthesisCode1
Improved Techniques for Training Single-Image GANsCode1
UnrealText: Synthesizing Realistic Scene Text Images from the Unreal WorldCode1
Synthesize then Compare: Detecting Failures and Anomalies for Semantic SegmentationCode1
A Content Transformation Block For Image Style TransferCode1
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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