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 3601–3625 of 6689 papers

TitleStatusHype
Diffusion idea exploration for art generation—0
Automatic Generation of Semantic Parts for Face Image SynthesisCode0
Cross-Modality Fourier Feature for Medical Image SynthesisCode0
Exact Diffusion Inversion via Bi-directional Integration ApproximationCode1
Divide, Evaluate, and Refine: Evaluating and Improving Text-to-Image Alignment with Iterative VQA Feedback—0
K-Space-Aware Cross-Modality Score for Synthesized Neuroimage Quality Assessment—0
DIFF-NST: Diffusion Interleaving For deFormable Neural Style Transfer—0
Score-based Conditional Generation with Fewer Labeled Data by Self-calibrating Classifier Guidance—0
Measuring the Success of Diffusion Models at Imitating Human Artists—0
Censored Sampling of Diffusion Models Using 3 Minutes of Human FeedbackCode1
On the Cultural Gap in Text-to-Image GenerationCode0
SoK: Privacy-Preserving Data Synthesis—0
On the Adversarial Robustness of Generative Autoencoders in the Latent Space—0
Prompting Diffusion Representations for Cross-Domain Semantic SegmentationCode0
Interpretable Computer Vision Models through Adversarial Training: Unveiling the Robustness-Interpretability ConnectionCode0
AdAM: Few-Shot Image Generation via Adaptation-Aware Kernel Modulation—0
Disentanglement in a GAN for Unconditional Speech SynthesisCode1
Training Energy-Based Models with Diffusion Contrastive Divergences—0
ECG-Image-Kit: A Synthetic Image Generation Toolbox to Facilitate Deep Learning-Based Electrocardiogram DigitizationCode1
SDXL: Improving Latent Diffusion Models for High-Resolution Image SynthesisCode2
MVDiffusion: Enabling Holistic Multi-view Image Generation with Correspondence-Aware DiffusionCode2
DifFSS: Diffusion Model for Few-Shot Semantic SegmentationCode1
Investigating Data Memorization in 3D Latent Diffusion Models for Medical Image Synthesis—0
Squeezing Large-Scale Diffusion Models for Mobile—0
LEDITS: Real Image Editing with DDPM Inversion and Semantic Guidance—0
Show:102550
← PrevPage 145 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