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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1826–1850 of 6689 papers

TitleStatusHype
MALI: A memory efficient and reverse accurate integrator for Neural ODEsCode1
Protecting Intellectual Property of Generative Adversarial Networks from Ambiguity AttackCode1
Generating images from caption and vice versa via CLIP-Guided Generative Latent Space SearchCode1
Applications of Deep Learning in Fundus Images: A ReviewCode1
GUIGAN: Learning to Generate GUI Designs Using Generative Adversarial NetworksCode1
Maximum Likelihood Training of Score-Based Diffusion ModelsCode1
Counterfactual Generative NetworksCode1
Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image SynthesisCode1
Cross-Modal Contrastive Learning for Text-to-Image GenerationCode1
GAN-Control: Explicitly Controllable GANsCode1
Knowledge Distillation in Iterative Generative Models for Improved Sampling SpeedCode1
LoFGAN: Fusing Local Representations for Few-Shot Image GenerationCode1
Image Synthesis From Layout With Locality-Aware Mask AdaptionCode1
Image Synthesis with Adversarial Networks: a Comprehensive Survey and Case StudiesCode1
EC-GAN: Low-Sample Classification using Semi-Supervised Algorithms and GANsCode1
Soft-IntroVAE: Analyzing and Improving the Introspective Variational AutoencoderCode1
Focal Frequency Loss for Image Reconstruction and SynthesisCode1
High-Fidelity Neural Human Motion Transfer from Monocular VideoCode1
Taming Transformers for High-Resolution Image SynthesisCode1
Self-Supervised Sketch-to-Image SynthesisCode1
Learning Self-Consistency for Deepfake DetectionCode1
Learning Energy-Based Models by Diffusion Recovery LikelihoodCode1
Human Pose Transfer by Adaptive Hierarchical DeformationCode1
Full-Glow: Fully conditional Glow for more realistic image generationCode1
Improving the Fairness of Deep Generative Models without RetrainingCode1
Show:102550
← PrevPage 74 of 268Next →
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