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 57515775 of 6689 papers

TitleStatusHype
Evaluating the Impact of Intensity Normalization on MR Image SynthesisCode0
Unsupervised Person Image Generation with Semantic Parsing TransformationCode0
Adversarial symmetric GANs: bridging adversarial samples and adversarial networksCode0
Peer-Ranked Precision: Creating a Foundational Dataset for Fine-Tuning Vision Models from DataSeeds' Annotated ImageryCode0
Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial NetworksCode0
Composite Functional Gradient Learning of Generative Adversarial ModelsCode0
Shadow Removal by High-Quality Shadow SynthesisCode0
Evidential Sparsification of Multimodal Latent Spaces in Conditional Variational AutoencodersCode0
EvoGAN: An Evolutionary Computation Assisted GANCode0
TaxaDiffusion: Progressively Trained Diffusion Model for Fine-Grained Species GenerationCode0
Diverse Conditional Image Generation by Stochastic Regression with Latent Drop-Out CodesCode0
DiamondGAN: Unified Multi-Modal Generative Adversarial Networks for MRI Sequences SynthesisCode0
Perceptual Gradient NetworksCode0
Exploration into Translation-Equivariant Image QuantizationCode0
Table and Image Generation for Investigating Knowledge of Entities in Pre-trained Vision and Language ModelsCode0
Breaking Free: How to Hack Safety Guardrails in Black-Box Diffusion Models!Code0
Medical Image Fusion for High-Level Analysis: A Mutual Enhancement Framework for Unaligned PAT and MRICode0
Exact Fusion via Feature Distribution Matching for Few-shot Image GenerationCode0
Image Translation for Medical Image Generation -- Ischemic Stroke LesionsCode0
VTNFP: An Image-Based Virtual Try-On Network With Body and Clothing Feature PreservationCode0
Meta-Learning and Self-Supervised Pretraining for Real World Image TranslationCode0
Reproducibility Study of "ITI-GEN: Inclusive Text-to-Image Generation"Code0
Imaginative Walks: Generative Random Walk Deviation Loss for Improved Unseen Learning RepresentationCode0
IMAGINE-E: Image Generation Intelligence Evaluation of State-of-the-art Text-to-Image ModelsCode0
Example-Guided Style Consistent Image Synthesis from Semantic LabelingCode0
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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