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 42014225 of 6689 papers

TitleStatusHype
QID^2: An Image-Conditioned Diffusion Model for Q-space Up-sampling of DWI Data0
QIRL: Boosting Visual Question Answering via Optimized Question-Image Relation Learning0
QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation0
QSMDiff: Unsupervised 3D Diffusion Models for Quantitative Susceptibility Mapping0
Qua^2SeDiMo: Quantifiable Quantization Sensitivity of Diffusion Models0
Qualitative Failures of Image Generation Models and Their Application in Detecting Deepfakes0
Quality analysis of DCGAN-generated mammography lesions0
Quality and Quantity: Unveiling a Million High-Quality Images for Text-to-Image Synthesis in Fashion Design0
Unpaired Translation from Semantic Label Maps to Images by Leveraging Domain-Specific Simulations0
Quantitative analysis of collagen remodeling in pancreatic lesions using computationally translated collagen images derived from brightfield microscopy images0
Quantitative Comparison of Fine-Tuning Techniques for Pretrained Latent Diffusion Models in the Generation of Unseen SAR Image Concepts0
Quantitative Manipulation of Custom Attributes on 3D-Aware Image Synthesis0
Zero-Shot Subject-Centric Generation for Creative Application Using Entropy Fusion0
Quantum Generative Models for Image Generation: Insights from MNIST and MedMNIST0
UnrealStereo: Controlling Hazardous Factors to Analyze Stereo Vision0
Would Deep Generative Models Amplify Bias in Future Models?0
Question-Conditioned Counterfactual Image Generation for VQA0
QUOTA: Quantifying Objects with Text-to-Image Models for Any Domain0
Quo Vadis ChatGPT? From Large Language Models to Large Knowledge Models0
R2I-Bench: Benchmarking Reasoning-Driven Text-to-Image Generation0
R3CD: Scene Graph to Image Generation with Relation-aware Compositional Contrastive Control Diffusion0
WuDaoMM: A large-scale Multi-Modal Dataset for Pre-training models0
RadGazeGen: Radiomics and Gaze-guided Medical Image Generation using Diffusion Models0
Radiological image synthesis using cycle-consistent generative adversarial network0
RAD: Region-Aware Diffusion Models for Image Inpainting0
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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