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 6101–6125 of 6689 papers

TitleStatusHype
Inspirational Adversarial Image Generation—0
Pose Guided Fashion Image Synthesis Using Deep Generative Model—0
Multi-Adversarial Variational Autoencoder Networks—0
Mask2Lesion: Mask-Constrained Adversarial Skin Lesion Image Synthesis—0
Stable Rank Normalization for Improved Generalization in Neural Networks and GANs—0
Parallel Scheduled Sampling—0
Shapes and Context: In-the-Wild Image Synthesis & Manipulation—0
Multi-objects Generation with Amortized Structural RegularizationCode0
The Implicit Metropolis-Hastings AlgorithmCode0
Labeled Graph Generative Adversarial Networks—0
Two-Stage Peer-Regularized Feature Recombination for Arbitrary Image Style TransferCode1
Image Synthesis with a Single (Robust) ClassifierCode0
Residual Flows for Invertible Generative ModelingCode1
Generative Adversarial Networks in Computer Vision: A Survey and TaxonomyCode1
Example-Guided Style Consistent Image Synthesis from Semantic LabelingCode0
cGANs with Conditional Convolution Layer—0
Generating Diverse High-Fidelity Images with VQ-VAE-2Code1
ZstGAN: An Adversarial Approach for Unsupervised Zero-Shot Image-to-Image TranslationCode0
Learning Patterns in Sample Distributions for Monte Carlo Variance Reduction—0
Coordinate-Based Texture Inpainting for Pose-Guided Human Image Generation—0
Feedback Adversarial Learning: Spatial Feedback for Improving Generative Adversarial Networks—0
Sphere Generative Adversarial Network Based on Geometric Moment MatchingCode0
Parallel Optimal Transport GAN—0
Example-Guided Style-Consistent Image Synthesis From Semantic Labeling—0
Multispectral Imaging for Fine-Grained Recognition of Powders on Complex Backgrounds—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