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 5851–5900 of 6689 papers

TitleStatusHype
Deep Image Spatial Transformation for Person Image GenerationCode1
Cross-Spectrum Dual-Subspace Pairing for RGB-infrared Cross-Modality Person Re-Identification—0
A U-Net Based Discriminator for Generative Adversarial NetworksCode1
Sparse Sinkhorn AttentionCode0
On Leveraging Pretrained GANs for Generation with Limited DataCode1
Performance Evaluation of Deep Generative Models for Generating Hand-Written Character Images—0
CookGAN: Meal Image Synthesis from IngredientsCode1
Unsupervised Discovery, Control, and Disentanglement of Semantic Attributes with Applications to Anomaly Detection—0
Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANsCode1
On Feature Normalization and Data AugmentationCode1
When Relation Networks meet GANs: Relation GANs with Triplet LossCode0
Reliable Fidelity and Diversity Metrics for Generative ModelsCode1
VFlow: More Expressive Generative Flows with Variational Data AugmentationCode1
Augmented Normalizing Flows: Bridging the Gap Between Generative Flows and Latent Variable ModelsCode1
4D Semantic Cardiac Magnetic Resonance Image Synthesis on XCAT Anatomical Model—0
Unsupervised Image-generation Enhanced Adaptation for Object Detection in Thermal images—0
Latent Normalizing Flows for Many-to-Many Cross-Domain MappingsCode1
Realistic River Image Synthesis using Deep Generative Adversarial Networks—0
Learning to Generate Levels From NothingCode1
Hi-Net: Hybrid-fusion Network for Multi-modal MR Image SynthesisCode1
Improved Consistency Regularization for GANs—0
Reconstructing the Noise Manifold for Image Denoising—0
Connecting GANs, MFGs, and OT—0
Exocentric to Egocentric Image Generation via Parallel Generative Adversarial Network—0
How to train your neural ODE: the world of Jacobian and kinetic regularizationCode1
Subspace Capsule NetworkCode1
Fixed smooth convolutional layer for avoiding checkerboard artifacts in CNNs—0
Pixel-wise Conditioned Generative Adversarial Networks for Image Synthesis and Completion—0
Improving the Evaluation of Generative Models with Fuzzy LogicCode0
Multi-Channel Attention Selection GANs for Guided Image-to-Image TranslationCode1
Designing GANs: A Likelihood Ratio Approach—0
A Generative Adversarial Network for AI-Aided Chair Design—0
Adversarial Code Learning for Image Generation—0
Correlation via Synthesis: End-to-end Image Generation and Radiogenomic Learning Based on Generative Adversarial Network—0
On the Role of Receptive Field in Unsupervised Sim-to-Real Image Translation—0
Instance Segmentation of Visible and Occluded Regions for Finding and Picking Target from a Pile of Objects—0
P^2-GAN: Efficient Style Transfer Using Single Style ImageCode0
Text-to-Image Generation with Attention Based Recurrent Neural Networks—0
Structured GANs—0
High-Fidelity Synthesis with Disentangled RepresentationCode1
180-degree Outpainting from a Single Image—0
Reformer: The Efficient TransformerCode2
Bridging the gap between AI and Healthcare sides: towards developing clinically relevant AI-powered diagnosis systems—0
Towards GAN Benchmarks Which Require Generalization—0
Spherical Image Generation from a Single Normal Field of View Image by Considering Scene Symmetry—0
Deep OCT Angiography Image Generation for Motion Artifact Suppression—0
Deceiving Image-to-Image Translation Networks for Autonomous Driving with Adversarial Perturbations—0
A Robust Pose Transformational GAN for Pose Guided Person Image Synthesis—0
FDFtNet: Facing Off Fake Images using Fake Detection Fine-tuning NetworkCode1
A Neural Dirichlet Process Mixture Model for Task-Free Continual LearningCode1
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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