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 5351–5400 of 6689 papers

TitleStatusHype
Adversarially robust segmentation models learn perceptually-aligned gradients—0
Estimating Fine-Grained Noise Model via Contrastive Learning—0
IR-GAN: Image Manipulation with Linguistic Instruction by Increment Reasoning—0
Fast and Automatic Object Registration for Human-Robot Collaboration in Industrial Manufacturing—0
Marginal Contrastive Correspondence for Guided Image Generation—0
Dressing in the Wild by Watching Dance Videos—0
AnyFace: Free-style Text-to-Face Synthesis and Manipulation—0
TL-GAN: Improving Traffic Light Recognition via Data Synthesis for Autonomous Driving—0
Robust deep learning for eye fundus images: Bridging real and synthetic data for enhancing generalizationCode0
Spatially Multi-conditional Image Generation—0
A DNN Optimizer that Improves over AdaBelief by Suppression of the Adaptive Stepsize RangeCode0
Bi-level Doubly Variational Learning for Energy-based Latent Variable Models—0
WuDaoMM: A large-scale Multi-Modal Dataset for Pre-training models—0
Review of Disentanglement Approaches for Medical Applications -- Towards Solving the Gordian Knot of Generative Models in Healthcare—0
Towards Device Efficient Conditional Image Generation—0
DU-VLG: Unifying Vision-and-Language Generation via Dual Sequence-to-Sequence Pre-training—0
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
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
3D-GIF: 3D-Controllable Object Generation via Implicit Factorized Representations—0
Human Silhouette and Skeleton Video Synthesis through Wi-Fi signals—0
Medical Image Segmentation on MRI Images with Missing Modalities: A Review—0
KPE: Keypoint Pose Encoding for Transformer-based Image GenerationCode0
Dynamic Dual-Output Diffusion Models—0
Signature and Log-signature for the Study of Empirical Distributions Generated with GANsCode0
Unsupervised Image Registration Towards Enhancing Performance and Explainability in Cardiac And Brain Image Analysis—0
Depth-SIMS: Semi-Parametric Image and Depth Synthesis—0
DrawingInStyles: Portrait Image Generation and Editing with Spatially Conditioned StyleGAN—0
Detecting High-Quality GAN-Generated Face Images using Neural Networks—0
Variational Autoencoders Without the Variation—0
State-of-the-Art in the Architecture, Methods and Applications of StyleGAN—0
One-shot Ultra-high-Resolution Generative Adversarial Network That Synthesizes 16K Images On A Single GPU—0
The Spectral Bias of Polynomial Neural Networks—0
se-Shweshwe Inspired Fashion Generation—0
OptGAN: Optimizing and Interpreting the Latent Space of the Conditional Text-to-Image GANs—0
Clarifying MCMC-based training of modern EBMs : Contrastive Divergence versus Maximum Likelihood—0
Transformers in Medical Image Analysis: A Review—0
Deepfake Detection for Facial Images with Facemasks—0
Synthesizing Photorealistic Images with Deep Generative Learning—0
Cut and Continuous Paste towards Real-time Deep Fall Detection—0
Benchmarking Generative Latent Variable Models for SpeechCode0
MIST GAN: Modality Imputation Using Style Transfer for MRI—0
Diversity in deep generative models and generative AICode0
3D-Aware Indoor Scene Synthesis with Depth Priors—0
How to Fill the Optimum Set? Population Gradient Descent with Harmless Diversity—0
Cross-Modality Neuroimage Synthesis: A SurveyCode0
Learning Fast Samplers for Diffusion Models by Differentiating Through Sample Quality—0
Deep Feature Rotation for Multimodal Image Style Transfer—0
Residual Aligned: Gradient Optimization for Non-Negative Image Synthesis—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