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 59766000 of 6689 papers

TitleStatusHype
Asymmetric Bias in Text-to-Image Generation with Adversarial AttacksCode0
MontageGAN: Generation and Assembly of Multiple Components by GANsCode0
More comprehensive facial inversion for more effective expression recognitionCode0
A Contrastive Compositional Benchmark for Text-to-Image Synthesis: A Study with Unified Text-to-Image Fidelity MetricsCode0
Instructing Text-to-Image Diffusion Models via Classifier-Guided Semantic OptimizationCode0
Dual-Stream Reciprocal Disentanglement Learning for Domain Adaptation Person Re-IdentificationCode0
Fine-Grained Alignment and Noise Refinement for Compositional Text-to-Image GenerationCode0
More Expressive Attention with Negative WeightsCode0
Fine-grained Cross-modal Fusion based Refinement for Text-to-Image SynthesisCode0
Fine-grained Forecasting Models Via Gaussian Process Blurring EffectCode0
Fine-Grained Image Generation from Bangla Text Description using Attentional Generative Adversarial NetworkCode0
Fine-Grained is Too Coarse: A Novel Data-Centric Approach for Efficient Scene Graph GenerationCode0
Fine-grained MRI Reconstruction using Attentive Selection Generative Adversarial NetworksCode0
Divide and Compose with Score Based Generative ModelsCode0
Trust the Critics: Generatorless and Multipurpose WGANs with Initial Convergence GuaranteesCode0
Deep residual inception encoder–decoder network for medical imaging synthesisCode0
Towards Explainable Fake Image Detection with Multi-Modal Large Language ModelsCode0
RL-based Stateful Neural Adaptive Sampling and Denoising for Real-Time Path TracingCode0
MotionCom: Automatic and Motion-Aware Image Composition with LLM and Video Diffusion PriorCode0
Plug-and-Play Priors as a Score-Based MethodCode0
TextCaps : Handwritten Character Recognition with Very Small DatasetsCode0
Motion Transfer-Driven intra-class data augmentation for Finger Vein RecognitionCode0
Will Large-scale Generative Models Corrupt Future Datasets?Code0
A Generative Model for Texture Synthesis based on Optimal Transport between Feature DistributionsCode0
Fine Tuning Text-to-Image Diffusion Models for Correcting Anomalous ImagesCode0
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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