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

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Showing 2451–2475 of 6689 papers

TitleStatusHype
Denoising and Regularization via Exploiting the Structural Bias of Convolutional GeneratorsCode0
Modeling Emotions and Ethics with Large Language ModelsCode0
Modular StoryGAN with Background and Theme Awareness for Story VisualizationCode0
More Expressive Attention with Negative WeightsCode0
Mobile Edge Generation-Enabled Digital Twin: Architecture Design and Research OpportunitiesCode0
DeLiGAN : Generative Adversarial Networks for Diverse and Limited DataCode0
BLADERUNNER: Rapid Countermeasure for Synthetic (AI-Generated) StyleGAN FacesCode0
MMaDA: Multimodal Large Diffusion Language ModelsCode0
Deformation equivariant cross-modality image synthesis with paired non-aligned training dataCode0
Mixture-of-Subspaces in Low-Rank AdaptationCode0
Deformable GANs for Pose-based Human Image GenerationCode0
MixNMatch: Multifactor Disentanglement and Encoding for Conditional Image GenerationCode0
DEff-GAN: Diverse Attribute Transfer for Few-Shot Image SynthesisCode0
Deferred Neural Rendering: Image Synthesis using Neural TexturesCode0
Black-Box Diagnosis and Calibration on GAN Intra-Mode Collapse: A Pilot StudyCode0
Model-based Cleaning of the QUILT-1M Pathology Dataset for Text-Conditional Image SynthesisCode0
Defending Neural Backdoors via Generative Distribution ModelingCode0
MINT: a Multi-modal Image and Narrative Text Dubbing Dataset for Foley Audio Content Planning and GenerationCode0
MintNet: Building Invertible Neural Networks with Masked ConvolutionsCode0
MirrorGAN: Learning Text-to-image Generation by RedescriptionCode0
MISFIT-V: Misaligned Image Synthesis and Fusion using Information from Thermal and VisualCode0
MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPOCode0
MIGS: Meta Image Generation from Scene GraphsCode0
Multi-Agent Multimodal Models for Multicultural Text to Image GenerationCode0
Deep residual inception encoder–decoder network for medical imaging synthesisCode0
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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