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 16011625 of 6689 papers

TitleStatusHype
AudioToken: Adaptation of Text-Conditioned Diffusion Models for Audio-to-Image GenerationCode1
Focal Frequency Loss for Image Reconstruction and SynthesisCode1
FooDI-ML: a large multi-language dataset of food, drinks and groceries images and descriptionsCode1
Diffusion-Based Scene Graph to Image Generation with Masked Contrastive Pre-TrainingCode1
MVPbev: Multi-view Perspective Image Generation from BEV with Test-time Controllability and GeneralizabilityCode1
DiffInfinite: Large Mask-Image Synthesis via Parallel Random Patch Diffusion in HistopathologyCode1
Contrastive Feature Loss for Image PredictionCode1
Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement LearningCode1
Negative Data AugmentationCode1
Contrasting Intra-Modal and Ranking Cross-Modal Hard Negatives to Enhance Visio-Linguistic Compositional UnderstandingCode1
Audio2Head: Audio-driven One-shot Talking-head Generation with Natural Head MotionCode1
Foreground-Background Separation through Concept Distillation from Generative Image Foundation ModelsCode1
Π-nets: Deep Polynomial Neural NetworksCode1
Network Bending of Diffusion Models for Audio-Visual GenerationCode1
Flow Contrastive Estimation of Energy-Based ModelsCode1
Synthetic-Neuroscore: Using A Neuro-AI Interface for Evaluating Generative Adversarial NetworksCode1
Continuous-Time Functional Diffusion ProcessesCode1
DiffusionCLIP: Text-Guided Diffusion Models for Robust Image ManipulationCode1
Bridging the Gap Between f-GANs and Wasserstein GANsCode1
DiffProtect: Generate Adversarial Examples with Diffusion Models for Facial Privacy ProtectionCode1
Continuous Speculative Decoding for Autoregressive Image GenerationCode1
NocPlace: Nocturnal Visual Place Recognition via Generative and Inherited Knowledge TransferCode1
Memory-Efficient 3D Denoising Diffusion Models for Medical Image ProcessingCode1
Distribution-Aware Data Expansion with Diffusion ModelsCode1
Continuous Language Generative FlowCode1
Show:102550
← PrevPage 65 of 268Next →

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