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

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Showing 4826–4850 of 6689 papers

TitleStatusHype
deepNIR: Datasets for generating synthetic NIR images and improved fruit detection system using deep learning techniques—0
Image Super-Resolution With Deep Variational Autoencoders—0
Diffusion Probabilistic Modeling for Video GenerationCode1
Attribute Group Editing for Reliable Few-shot Image GenerationCode1
Fantastic Style Channels and Where to Find Them: A Submodular Framework for Discovering Diverse Directions in GANs—0
APRNet: Attention-based Pixel-wise Rendering Network for Photo-Realistic Text Image Generation—0
Things not Written in Text: Exploring Spatial Commonsense from Visual SignalsCode1
InsetGAN for Full-Body Image GenerationCode1
3D-GIF: 3D-Controllable Object Generation via Implicit Factorized Representations—0
Medical Image Segmentation on MRI Images with Missing Modalities: A Review—0
Human Silhouette and Skeleton Video Synthesis through Wi-Fi signals—0
FlexIT: Towards Flexible Semantic Image TranslationCode1
KPE: Keypoint Pose Encoding for Transformer-based Image GenerationCode0
Dynamic Dual-Output Diffusion Models—0
Unsupervised Image Registration Towards Enhancing Performance and Explainability in Cardiac And Brain Image Analysis—0
Signature and Log-signature for the Study of Empirical Distributions Generated with GANsCode0
Depth-SIMS: Semi-Parametric Image and Depth Synthesis—0
Exploring Dual-task Correlation for Pose Guided Person Image GenerationCode1
DrawingInStyles: Portrait Image Generation and Editing with Spatially Conditioned StyleGAN—0
Pseudo-Stereo for Monocular 3D Object Detection in Autonomous DrivingCode1
Interactive Image Synthesis with Panoptic Layout GenerationCode1
Detecting High-Quality GAN-Generated Face Images using Neural Networks—0
Autoregressive Image Generation using Residual QuantizationCode3
Polarity Sampling: Quality and Diversity Control of Pre-Trained Generative Networks via Singular ValuesCode1
PetsGAN: Rethinking Priors for Single Image GenerationCode1
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All datasetsImageNet 256x256CIFAR-10ImageNet 64x64ImageNet 512x512FFHQ 256 x 256CelebA 64x64ImageNet 32x32LSUN Bedroom 256 x 256STL-10LSUN Churches 256 x 256ImageNet 128x128FFHQ 1024 x 1024

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Improved DDPMFID12.3—Unverified
2ADMFID11.84—Unverified
3BigGAN-deepFID8.1—Unverified
4Polarity-BigGANFID6.82—Unverified
5VQGAN+Transformer (k=mixed, p=1.0, a=0.005)FID6.59—Unverified
6MaskGITFID6.18—Unverified
7VQGAN+Transformer (k=600, p=1.0, a=0.05)FID5.2—Unverified
8CDMFID4.88—Unverified
9ADM-GFID4.59—Unverified
10RINFID4.51—Unverified
#ModelMetricClaimedVerifiedStatus
1PresGANFID52.2—Unverified
2RESFLOWFID48.29—Unverified
3Residual FlowFID46.37—Unverified
4GLF+perceptual loss (ours)FID44.6—Unverified
5ProdPoly no activation functionsFID40.45—Unverified
6ProdPoly no activation functionsFID36.77—Unverified
7ACGANFID35.47—Unverified
8DenseFlow-74-10FID34.9—Unverified
9NVAE w/ flowFID32.53—Unverified
10QSNGANFID31.97—Unverified
#ModelMetricClaimedVerifiedStatus
1GLIDE + CLSFID30.87—Unverified
2GLIDE + CLIPFID30.46—Unverified
3GLIDE + CLS-FREEFID29.22—Unverified
4GLIDE + CLIP + CLS + CLS-FREEFID29.18—Unverified
5PGMGANFID21.73—Unverified
6CLR-GANFID20.27—Unverified
7FMFID14.45—Unverified
8CT (Direct Generation, NFE=1)FID13—Unverified
9CT (Direct Generation, NFE=2)FID11.1—Unverified
10GLIDE +CLSKID7.95—Unverified