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 6076–6100 of 6689 papers

TitleStatusHype
SynthText3D: Synthesizing Scene Text Images from 3D Virtual WorldsCode0
Jointly Adversarial Network to Wavelength Compensation and Dehazing of Underwater Images—0
Adversarial Lipschitz RegularizationCode0
Generative Modeling by Estimating Gradients of the Data DistributionCode2
M3D-GAN: Multi-Modal Multi-Domain Translation with Universal Attention—0
Mean Spectral Normalization of Deep Neural Networks for Embedded AutomationCode1
Correlation via synthesis: end-to-end nodule image generation and radiogenomic map learning based on generative adversarial network—0
Copula & Marginal Flows: Disentangling the Marginal from its Joint—0
Dual Adversarial Learning with Attention Mechanism for Fine-grained Medical Image Synthesis—0
Deep Learning for Fine-Grained Image Analysis: A SurveyCode0
Twin Auxiliary Classifiers GANCode0
Guided Image Generation with Conditional Invertible Neural NetworksCode1
Large Scale Adversarial Representation LearningCode1
Robust Synthesis of Adversarial Visual Examples Using a Deep Image Prior—0
Mask Embedding in conditional GAN for Guided Synthesis of High Resolution ImagesCode0
Obj-GloVe: Scene-Based Contextual Object Embedding—0
Generative Guiding Block: Synthesizing Realistic Looking Variants Capable of Even Large Change Demands—0
Fully automatic computer-aided mass detection and segmentation via pseudo-color mammograms and Mask R-CNNCode1
Abnormal Colon Polyp Image Synthesis Using Conditional Adversarial Networks for Improved Detection Performance—0
Adversarial Pixel-Level Generation of Semantic Images—0
A Convolutional Decoder for Point Clouds using Adaptive Instance Normalization—0
AGAN: Towards Automated Design of Generative Adversarial Networks—0
Interactive Optimization of Generative Image Modeling using Sequential Subspace Search and Content-based Guidance—0
Cross-Domain Conditional Generative Adversarial Networks for Stereoscopic Hyperrealism in Surgical Training—0
Scene Text Magnifier—0
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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