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 6151–6200 of 6689 papers

TitleStatusHype
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
Robust Synthesis of Adversarial Visual Examples Using a Deep Image Prior—0
Mask Embedding in conditional GAN for Guided Synthesis of High Resolution ImagesCode0
Generative Guiding Block: Synthesizing Realistic Looking Variants Capable of Even Large Change Demands—0
Obj-GloVe: Scene-Based Contextual Object Embedding—0
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
Inspirational Adversarial Image Generation—0
Pose Guided Fashion Image Synthesis Using Deep Generative Model—0
Scene Text Magnifier—0
Multi-Adversarial Variational Autoencoder Networks—0
Mask2Lesion: Mask-Constrained Adversarial Skin Lesion Image Synthesis—0
Parallel Scheduled Sampling—0
Shapes and Context: In-the-Wild Image Synthesis & Manipulation—0
Stable Rank Normalization for Improved Generalization in Neural Networks and GANs—0
Multi-objects Generation with Amortized Structural RegularizationCode0
The Implicit Metropolis-Hastings AlgorithmCode0
Labeled Graph Generative Adversarial Networks—0
Image Synthesis with a Single (Robust) ClassifierCode0
Example-Guided Style Consistent Image Synthesis from Semantic LabelingCode0
cGANs with Conditional Convolution Layer—0
Sphere Generative Adversarial Network Based on Geometric Moment MatchingCode0
Coordinate-Based Texture Inpainting for Pose-Guided Human Image Generation—0
Feedback Adversarial Learning: Spatial Feedback for Improving Generative Adversarial Networks—0
ZstGAN: An Adversarial Approach for Unsupervised Zero-Shot Image-to-Image TranslationCode0
Example-Guided Style-Consistent Image Synthesis From Semantic Labeling—0
Parallel Optimal Transport GAN—0
Multispectral Imaging for Fine-Grained Recognition of Powders on Complex Backgrounds—0
Learning Patterns in Sample Distributions for Monte Carlo Variance Reduction—0
Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection—0
KG-GAN: Knowledge-Guided Generative Adversarial Networks—0
Training Generative Adversarial Networks from Incomplete Observations using Factorised DiscriminatorsCode0
JGAN: A Joint Formulation of GAN for Synthesizing Images and Labels—0
Style transfer-based image synthesis as an efficient regularization technique in deep learning—0
Precision-Recall Curves Using Information Divergence Frontiers—0
Synthesizing Images from Spatio-Temporal Representations using Spike-based Backpropagation—0
Generative Latent FlowCode0
Decentralized Learning of Generative Adversarial Networks from Non-iid Data—0
Training language GANs from ScratchCode0
AttentionRNN: A Structured Spatial Attention Mechanism—0
Using Photorealistic Face Synthesis and Domain Adaptation to Improve Facial Expression Analysis—0
Non-Parametric Priors For Generative Adversarial Networks—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