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 5226–5250 of 6689 papers

TitleStatusHype
Would Deep Generative Models Amplify Bias in Future Models?—0
Question-Conditioned Counterfactual Image Generation for VQA—0
QUOTA: Quantifying Objects with Text-to-Image Models for Any Domain—0
Quo Vadis ChatGPT? From Large Language Models to Large Knowledge Models—0
R2I-Bench: Benchmarking Reasoning-Driven Text-to-Image Generation—0
R3CD: Scene Graph to Image Generation with Relation-aware Compositional Contrastive Control Diffusion—0
WuDaoMM: A large-scale Multi-Modal Dataset for Pre-training models—0
RadGazeGen: Radiomics and Gaze-guided Medical Image Generation using Diffusion Models—0
Radiological image synthesis using cycle-consistent generative adversarial network—0
RAD: Region-Aware Diffusion Models for Image Inpainting—0
RAGAR: Retrieval Augment Personalized Image Generation Guided by Recommendation—0
RaggeDi: Diffusion-based State Estimation of Disordered Rags, Sheets, Towels and Blankets—0
RainSD: Rain Style Diversification Module for Image Synthesis Enhancement using Feature-Level Style Distribution—0
Rain structure transfer using an exemplar rain image for synthetic rain image generation—0
RANA: Relightable Articulated Neural Avatars—0
Align, Adapt and Inject: Sound-guided Unified Image Generation—0
Efficient Video Prediction via Sparsely Conditioned Flow Matching—0
Randomize to Generalize: Domain Randomization for Runway FOD Detection—0
Random Multi-Channel Image Synthesis for Multiplexed Immunofluorescence Imaging—0
Random Network Distillation as a Diversity Metric for Both Image and Text Generation—0
Ranking CGANs: Subjective Control over Semantic Image Attributes—0
Rapid Review of Generative AI in Smart Medical Applications—0
Discovery and Expansion of New Domains within Diffusion Models—0
Unseen Visual Anomaly Generation—0
Raw Bayer Pattern Image Synthesis for Computer Vision-oriented Image Signal Processing Pipeline Design—0
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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