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 6551–6575 of 6689 papers

TitleStatusHype
Generative OpenMax for Multi-Class Open Set Classification—0
Semantic Image Synthesis via Adversarial LearningCode1
Guiding InfoGAN with Semi-SupervisionCode0
Adversarial Generation of Training Examples: Applications to Moving Vehicle License Plate Recognition—0
Laplacian-Steered Neural Style TransferCode0
Dual Supervised Learning—0
Disentangled Representation Learning GAN for Pose-Invariant Face Recognition—0
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash EquilibriumCode1
DOTE: Dual cOnvolutional filTer lEarning for Super-Resolution and Cross-Modality Synthesis in MRI—0
Recent Progress of Face Image Synthesis—0
TextureGAN: Controlling Deep Image Synthesis with Texture PatchesCode0
Sliced Wasserstein Generative ModelsCode1
DeLiGAN : Generative Adversarial Networks for Diverse and Limited DataCode0
Depth Structure Preserving Scene Image Generation—0
Megapixel Size Image Creation using Generative Adversarial Networks—0
Representation Learning by Rotating Your Faces—0
Adversarial Generation of Natural Language—0
Stabilizing Training of Generative Adversarial Networks through RegularizationCode0
Pose Guided Person Image GenerationCode0
From source to target and back: symmetric bi-directional adaptive GAN—0
Semantically Decomposing the Latent Spaces of Generative Adversarial NetworksCode0
Multi-Stage Variational Auto-Encoders for Coarse-to-Fine Image GenerationCode0
Relaxed Wasserstein with Applications to GANs—0
Gradient Estimators for Implicit ModelsCode0
Learning Texture Manifolds with the Periodic Spatial GANCode0
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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