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 28012825 of 6689 papers

TitleStatusHype
DefectFill: Realistic Defect Generation with Inpainting Diffusion Model for Visual Inspection0
DE-FAKE: Detection and Attribution of Fake Images Generated by Text-to-Image Generation Models0
DeepWarp: Photorealistic Image Resynthesis for Gaze Manipulation0
BitsFusion: 1.99 bits Weight Quantization of Diffusion Model0
A Deep and Tractable Density Estimator0
Deep super resolution crack network (SrcNet) for improving computer vision–based automated crack detectability in in situ bridges0
BitMark for Infinity: Watermarking Bitwise Autoregressive Image Generative Models0
Deep Sinogram Completion with Image Prior for Metal Artifact Reduction in CT Images0
Deep Shading: Convolutional Neural Networks for Screen-Space Shading0
deepSELF: An Open Source Deep Self End-to-End Learning Framework0
Bi-parametric prostate MR image synthesis using pathology and sequence-conditioned stable diffusion0
AniFaceDrawing: Anime Portrait Exploration during Your Sketching0
Image Augmentations for GAN Training0
Image Compression with Product Quantized Masked Image Modeling0
Image Generation and Translation with Disentangled Representations0
BiomedJourney: Counterfactual Biomedical Image Generation by Instruction-Learning from Multimodal Patient Journeys0
DeepPortraitDrawing: Generating Human Body Images from Freehand Sketches0
3D Neural Field Generation using Triplane Diffusion0
Deep OCT Angiography Image Generation for Motion Artifact Suppression0
Biologically Inspired Hexagonal Deep Learning for Hexagonal Image Generation0
Deep Nonparametric Convexified Filtering for Computational Photography, Image Synthesis and Adversarial Defense0
deepNIR: Datasets for generating synthetic NIR images and improved fruit detection system using deep learning techniques0
Addressing Image Hallucination in Text-to-Image Generation through Factual Image Retrieval0
Illustrious: an Open Advanced Illustration Model0
Image2Text2Image: A Novel Framework for Label-Free Evaluation of Image-to-Text Generation with Text-to-Image Diffusion Models0
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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