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 6051–6075 of 6689 papers

TitleStatusHype
Image Synthesis From Reconfigurable Layout and StyleCode1
Fully Automated Image De-fencing using Conditional Generative Adversarial Networks—0
Unpaired Image-to-Speech Synthesis with Multimodal Information BottleneckCode0
Dual Adversarial Inference for Text-to-Image Synthesis—0
AutoGAN: Neural Architecture Search for Generative Adversarial NetworksCode0
Enforcing Perceptual Consistency on Generative Adversarial Networks by Using the Normalised Laplacian Pyramid Distance—0
Editing Text in the WildCode0
Relighting Humans: Occlusion-Aware Inverse Rendering for Full-Body Human Images—0
SkrGAN: Sketching-rendering Unconditional Generative Adversarial Networks for Medical Image Synthesis—0
Visual-Relation Conscious Image Generation from Structured-Text—0
GAN Path Finder: Preliminary resultsCode1
Cycle In Cycle Generative Adversarial Networks for Keypoint-Guided Image GenerationCode1
InSituNet: Deep Image Synthesis for Parameter Space Exploration of Ensemble Simulations—0
An Empirical Study of Batch Normalization and Group Normalization in Conditional Computation—0
Uncertainty Quantification in Deep Learning for Safer Neuroimage Enhancement—0
Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver Segmentation—0
GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent RepresentationsCode1
A Two Stage GAN for High Resolution Retinal Image Generation and Segmentation—0
Variational f-divergence Minimization—0
Latent Space Factorisation and Manipulation via Matrix Subspace ProjectionCode0
Interpreting the Latent Space of GANs for Semantic Face EditingCode2
Warp and Learn: Novel Views Generation for Vehicles and Other ObjectsCode0
Discriminative Consistent Domain Generation for Semi-supervised Learning—0
Lifelong GAN: Continual Learning for Conditional Image Generation—0
MintNet: Building Invertible Neural Networks with Masked ConvolutionsCode0
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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