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 5951–6000 of 6689 papers

TitleStatusHype
StrokeCoder: Path-Based Image Generation from Single Examples using Transformers—0
Toward Accurate and Realistic Virtual Try-on Through Shape Matching and Multiple Warps—0
SwapText: Image Based Texts Transfer in Scenes—0
Blur, Noise, and Compression Robust Generative Adversarial Networks—0
Object-Centric Image Generation from Layouts—0
BigGAN-based Bayesian reconstruction of natural images from human brain activity—0
Your GAN is Secretly an Energy-based Model and You Should use Discriminator Driven Latent SamplingCode0
Deep Domain-Adversarial Image Generation for Domain Generalisation—0
Anysize GAN: A solution to the image-warping problem—0
Hierarchical Modes Exploring in Generative Adversarial Networks—0
Unlimited Resolution Image Generation with R2D2-GANs—0
Cross-Spectrum Dual-Subspace Pairing for RGB-infrared Cross-Modality Person Re-Identification—0
Performance Evaluation of Deep Generative Models for Generating Hand-Written Character Images—0
Sparse Sinkhorn AttentionCode0
Unsupervised Discovery, Control, and Disentanglement of Semantic Attributes with Applications to Anomaly Detection—0
When Relation Networks meet GANs: Relation GANs with Triplet LossCode0
Unsupervised Image-generation Enhanced Adaptation for Object Detection in Thermal images—0
4D Semantic Cardiac Magnetic Resonance Image Synthesis on XCAT Anatomical Model—0
Realistic River Image Synthesis using Deep Generative Adversarial Networks—0
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
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
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
P^2-GAN: Efficient Style Transfer Using Single Style ImageCode0
Instance Segmentation of Visible and Occluded Regions for Finding and Picking Target from a Pile of Objects—0
Text-to-Image Generation with Attention Based Recurrent Neural Networks—0
Structured GANs—0
180-degree Outpainting from a Single Image—0
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
A^3DSegNet: Anatomy-aware artifact disentanglement and segmentation network for unpaired segmentation, artifact reduction, and modality translation—0
Pal-GAN: Palette-conditioned Generative Adversarial Networks—0
Explicit Gradient Learning for Black-Box Optimization—0
Image-guided Neural Object Rendering—0
OneGAN: Simultaneous Unsupervised Learning of Conditional Image Generation, Foreground Segmentation, and Fine-Grained Clustering—0
FHDR: HDR Image Reconstruction from a Single LDR Image using Feedback NetworkCode0
RPGAN: GANs Interpretability via Random RoutingCode0
Axial Attention in Multidimensional TransformersCode0
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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