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

Showing 58765900 of 6689 papers

TitleStatusHype
Subspace Capsule NetworkCode1
Fixed smooth convolutional layer for avoiding checkerboard artifacts in CNNs0
Pixel-wise Conditioned Generative Adversarial Networks for Image Synthesis and Completion0
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 Approach0
A Generative Adversarial Network for AI-Aided Chair Design0
Adversarial Code Learning for Image Generation0
Correlation via Synthesis: End-to-end Image Generation and Radiogenomic Learning Based on Generative Adversarial Network0
On the Role of Receptive Field in Unsupervised Sim-to-Real Image Translation0
Instance Segmentation of Visible and Occluded Regions for Finding and Picking Target from a Pile of Objects0
P^2-GAN: Efficient Style Transfer Using Single Style ImageCode0
Text-to-Image Generation with Attention Based Recurrent Neural Networks0
Structured GANs0
High-Fidelity Synthesis with Disentangled RepresentationCode1
180-degree Outpainting from a Single Image0
Reformer: The Efficient TransformerCode2
Bridging the gap between AI and Healthcare sides: towards developing clinically relevant AI-powered diagnosis systems0
Towards GAN Benchmarks Which Require Generalization0
Spherical Image Generation from a Single Normal Field of View Image by Considering Scene Symmetry0
Deep OCT Angiography Image Generation for Motion Artifact Suppression0
Deceiving Image-to-Image Translation Networks for Autonomous Driving with Adversarial Perturbations0
A Robust Pose Transformational GAN for Pose Guided Person Image Synthesis0
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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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Improved DDPMFID12.3Unverified
2ADMFID11.84Unverified
3BigGAN-deepFID8.1Unverified
4Polarity-BigGANFID6.82Unverified
5VQGAN+Transformer (k=mixed, p=1.0, a=0.005)FID6.59Unverified
6MaskGITFID6.18Unverified
7VQGAN+Transformer (k=600, p=1.0, a=0.05)FID5.2Unverified
8CDMFID4.88Unverified
9ADM-GFID4.59Unverified
10RINFID4.51Unverified
#ModelMetricClaimedVerifiedStatus
1PresGANFID52.2Unverified
2RESFLOWFID48.29Unverified
3Residual FlowFID46.37Unverified
4GLF+perceptual loss (ours)FID44.6Unverified
5ProdPoly no activation functionsFID40.45Unverified
6ProdPoly no activation functionsFID36.77Unverified
7ACGANFID35.47Unverified
8DenseFlow-74-10FID34.9Unverified
9NVAE w/ flowFID32.53Unverified
10QSNGANFID31.97Unverified
#ModelMetricClaimedVerifiedStatus
1GLIDE + CLSFID30.87Unverified
2GLIDE + CLIPFID30.46Unverified
3GLIDE + CLS-FREEFID29.22Unverified
4GLIDE + CLIP + CLS + CLS-FREEFID29.18Unverified
5PGMGANFID21.73Unverified
6CLR-GANFID20.27Unverified
7FMFID14.45Unverified
8CT (Direct Generation, NFE=1)FID13Unverified
9CT (Direct Generation, NFE=2)FID11.1Unverified
10GLIDE +CLSKID7.95Unverified