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 18511875 of 6689 papers

TitleStatusHype
Efficient Semantic Image Synthesis via Class-Adaptive NormalizationCode1
You Only Need Adversarial Supervision for Semantic Image SynthesisCode1
UnrealPerson: An Adaptive Pipeline towards Costless Person Re-identificationCode1
TediGAN: Text-Guided Diverse Face Image Generation and ManipulationCode1
Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar ReconstructionCode1
Adaptive Weighted Discriminator for Training Generative Adversarial NetworksCode1
Spectral Distribution Aware Image GenerationCode1
CoCosNet v2: Full-Resolution Correspondence Learning for Image TranslationCode1
Learning Two-Stream CNN for Multi-Modal Age-related Macular Degeneration CategorizationCode1
pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image SynthesisCode1
Improved Contrastive Divergence Training of Energy Based ModelsCode1
Learning Semantic-aware Normalization for Generative Adversarial NetworksCode1
Refining Deep Generative Models via Discriminator Gradient FlowCode1
S2FGAN: Semantically Aware Interactive Sketch-to-Face TranslationCode1
Overcoming Barriers to Data Sharing with Medical Image Generation: A Comprehensive EvaluationCode1
Hijack-GAN: Unintended-Use of Pretrained, Black-Box GANsCode1
Frequency Domain Image Translation: More Photo-realistic, Better Identity-preservingCode1
Image Generators with Conditionally-Independent Pixel SynthesisCode1
Score-Based Generative Modeling through Stochastic Differential EquationsCode1
Omni-GAN: On the Secrets of cGANs and BeyondCode1
Adversarial Generation of Continuous ImagesCode1
Synthesizing Optical and SAR Imagery From Land Cover Maps and Auxiliary Raster DataCode1
HistoGAN: Controlling Colors of GAN-Generated and Real Images via Color HistogramsCode1
Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on ImagesCode1
MUST-GAN: Multi-level Statistics Transfer for Self-driven Person Image GenerationCode1
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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