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 6576–6600 of 6689 papers

TitleStatusHype
Pixel Deconvolutional NetworksCode0
GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired DataCode0
Generative Cooperative Net for Image Generation and Data Augmentation—0
Simultaneous Super-Resolution and Cross-Modality Synthesis of 3D Medical Images using Weakly-Supervised Joint Convolutional Sparse Coding—0
Auto-painter: Cartoon Image Generation from Sketch by Using Conditional Generative Adversarial NetworksCode0
Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution—0
A Unified Approach of Multi-scale Deep and Hand-crafted Features for Defocus EstimationCode0
Supervised Adversarial Networks for Image Saliency Detection—0
Multi-View Image Generation from a Single-View—0
MAGAN: Margin Adaptation for Generative Adversarial NetworksCode0
How to Make an Image More Memorable? A Deep Style Transfer ApproachCode1
Unsupervised Holistic Image Generation from Key Local PatchesCode0
BEGAN: Boundary Equilibrium Generative Adversarial NetworksCode0
Improved Training of Wasserstein GANsCode1
CVAE-GAN: Fine-Grained Image Generation through Asymmetric TrainingCode1
GP-GAN: Towards Realistic High-Resolution Image BlendingCode0
I2T2I: Learning Text to Image Synthesis with Textual Data Augmentation—0
SurfNet: Generating 3D shape surfaces using deep residual networksCode0
Parallel Multiscale Autoregressive Density Estimation—0
Transformation-Grounded Image Generation Network for Novel 3D View SynthesisCode0
Triple Generative Adversarial NetsCode0
LR-GAN: Layered Recursive Generative Adversarial Networks for Image GenerationCode0
Generating Steganographic Images via Adversarial TrainingCode0
SSPP-DAN: Deep Domain Adaptation Network for Face Recognition with Single Sample Per PersonCode0
ArtGAN: Artwork Synthesis with Conditional Categorical GANsCode0
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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