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 57265750 of 6689 papers

TitleStatusHype
Estimating Object Physical Properties from RGB-D Vision and Depth Robot Sensors Using Deep LearningCode0
Pathology Synthesis of 3D-Consistent Cardiac MR Images using 2D VAEs and GANsCode0
Image Inpainting via Tractable Steering of Diffusion ModelsCode0
What Do You Want? User-centric Prompt Generation for Text-to-image Synthesis via Multi-turn GuidanceCode0
Time Step Generating: A Universal Synthesized Deepfake Image DetectorCode0
Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model ViewsCode0
Image Processing Using Multi-Code GAN PriorCode0
Black-Box Diagnosis and Calibration on GAN Intra-Mode Collapse: A Pilot StudyCode0
Evaluating and Predicting Distorted Human Body Parts for Generated ImagesCode0
Patronus: Bringing Transparency to Diffusion Models with PrototypesCode0
CFCPalsy: Facial Image Synthesis with Cross-Fusion Cycle Diffusion Model for Facial Paralysis IndividualsCode0
AttrLostGAN: Attribute Controlled Image Synthesis from Reconfigurable Layout and StyleCode0
Attributing Fake Images to GANs: Learning and Analyzing GAN FingerprintsCode0
Trade-offs in Fine-tuned Diffusion Models Between Accuracy and InterpretabilityCode0
MemControl: Mitigating Memorization in Diffusion Models via Automated Parameter SelectionCode0
Images Speak Volumes: User-Centric Assessment of Image Generation for Accessible CommunicationCode0
Image Style Transfer Using Convolutional Neural NetworksCode0
Evaluating Semantic Variation in Text-to-Image Synthesis: A Causal PerspectiveCode0
Evaluating Text-to-Image Generative Models: An Empirical Study on Human Image SynthesisCode0
PCGAN: Partition-Controlled Human Image GenerationCode0
VTGAN: Semi-supervised Retinal Image Synthesis and Disease Prediction using Vision TransformersCode0
Deepfake Detection without Deepfakes: Generalization via Synthetic Frequency Patterns InjectionCode0
Repairing Catastrophic-Neglect in Text-to-Image Diffusion Models via Attention-Guided Feature EnhancementCode0
Synthesizing Multi-Tracer PET Images for Alzheimer's Disease Patients using a 3D Unified Anatomy-aware Cyclic Adversarial NetworkCode0
SGDraw: Scene Graph Drawing Interface Using Object-Oriented RepresentationCode0
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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