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 61016125 of 6689 papers

TitleStatusHype
SmartBrush: Text and Shape Guided Object Inpainting with Diffusion ModelCode0
PQPP: A Joint Benchmark for Text-to-Image Prompt and Query Performance PredictionCode0
Gradient penalty from a maximum margin perspectiveCode0
DuoDiff: Accelerating Diffusion Models with a Dual-Backbone ApproachCode0
Multimodal Latent Language Modeling with Next-Token DiffusionCode0
Tackling Copyright Issues in AI Image Generation Through Originality Estimation and GenericizationCode0
Twin Auxilary Classifiers GANCode0
VITON-DRR: Details Retention Virtual Try-on via Non-rigid RegistrationCode0
Twin Auxiliary Classifiers GANCode0
Stochastic Adaptive Activation FunctionCode0
Multi-objective Deep Data Generation with Correlated Property ControlCode0
Frequency-Supervised MR-to-CT Image SynthesisCode0
Multi-objective evolutionary GAN for tabular data synthesisCode0
Joint Learning of Neural Networks via Iterative Reweighted Least SquaresCode0
Multi-Objective Quality-Diversity in Unstructured and Unbounded SpacesCode0
Rob-GAN: Generator, Discriminator, and Adversarial AttackerCode0
Multi-objects Generation with Amortized Structural RegularizationCode0
Precision or Recall? An Analysis of Image Captions for Training Text-to-Image Generation ModelCode0
From Easy to Hard: Building a Shortcut for Differentially Private Image SynthesisCode0
TextNeRF: A Novel Scene-Text Image Synthesis Method based on Neural Radiance FieldsCode0
Defending Neural Backdoors via Generative Distribution ModelingCode0
Gotta Adapt 'Em All: Joint Pixel and Feature-Level Domain Adaptation for Recognition in the WildCode0
CAT Pruning: Cluster-Aware Token Pruning For Text-to-Image Diffusion ModelsCode0
From Keypoints to Object Landmarks via Self-Training Correspondence: A novel approach to Unsupervised Landmark DiscoveryCode0
Multi-Resolution Continuous Normalizing FlowsCode0
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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