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 30263050 of 6689 papers

TitleStatusHype
Indonesian Text-to-Image Synthesis with Sentence-BERT and FastGANCode0
A Survey on fMRI-based Brain Decoding for Reconstructing Multimodal StimuliCode0
Improving GANs Using Optimal TransportCode0
CONCORD: Concept-Informed Diffusion for Dataset DistillationCode0
Enriching Information and Preserving Semantic Consistency in Expanding Curvilinear Object Segmentation DatasetsCode0
Improving Diffusion-Based Generative Models via Approximated Optimal TransportCode0
Improving Compositional Generation with Diffusion Models Using Lift ScoresCode0
Improving Explicit Spatial Relationships in Text-to-Image Generation through an Automatically Derived DatasetCode0
Improved Variational Inference with Inverse Autoregressive FlowCode0
Enhancing Quantitative Image Synthesis through Pretraining and Resolution Scaling for Bone Mineral Density Estimation from a Plain X-ray ImageCode0
Concept Replacer: Replacing Sensitive Concepts in Diffusion Models via Precision LocalizationCode0
Improving MMD-GAN Training with Repulsive Loss FunctionCode0
Enhancing Object Coherence in Layout-to-Image SynthesisCode0
Improved Modeling of 3D Shapes with Multi-view Depth MapsCode0
Improved Conditional Flow Models for Molecule to Image SynthesisCode0
Enhancing Image Generation Fidelity via Progressive PromptsCode0
Enhancing GANs with MMD Neural Architecture Search, PMish Activation Function, and Adaptive Rank DecompositionCode0
Improved ArtGAN for Conditional Synthesis of Natural Image and ArtworkCode0
Implicit Inversion turns CLIP into a DecoderCode0
Enhancing GAN Performance through Neural Architecture Search and Tensor DecompositionCode0
Implicit Generation and Modeling with Energy Based ModelsCode0
Implicit Generative CopulasCode0
Implicit competitive regularization in GANsCode0
ZstGAN: An Adversarial Approach for Unsupervised Zero-Shot Image-to-Image TranslationCode0
Enhancing Diffusion Models Efficiency by Disentangling Total-Variance and Signal-to-Noise RatioCode0
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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