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 5201–5225 of 6689 papers

TitleStatusHype
SITTA: Single Image Texture Translation for Data AugmentationCode1
NP-DRAW: A Non-Parametric Structured Latent Variable Model for Image GenerationCode1
Where is the disease? Semi-supervised pseudo-normality synthesis from an abnormal image—0
Handling Data Heterogeneity with Generative Replay in Collaborative Learning for Medical Imaging—0
GaussiGAN: Controllable Image Synthesis with 3D Gaussians from Unposed Silhouettes—0
Generative Self-training for Cross-domain Unsupervised Tagged-to-Cine MRI Synthesis—0
Feature Alignment as a Generative ProcessCode0
Alias-Free Generative Adversarial NetworksCode3
Fairness for Image Generation with Uncertain Sensitive AttributesCode1
Total Generate: Cycle in Cycle Generative Adversarial Networks for Generating Human Faces, Hands, Bodies, and Natural ScenesCode1
Attack to Fool and Explain Deep Networks—0
Manifold Matching via Deep Metric Learning for Generative ModelingCode1
Mask-Embedded Discriminator With Region-Based Semantic Regularization for Semi-Supervised Class-Conditional Image Synthesis—0
Adaptive Convolutions for Structure-Aware Style TransferCode1
Mol2Image: Improved Conditional Flow Models for Molecule to Image Synthesis—0
Brain Image Synthesis With Unsupervised Multivariate Canonical CSCl4Net—0
Self-Supervised 3D Mesh Reconstruction From Single Images—0
Protecting Intellectual Property of Generative Adversarial Networks From Ambiguity Attacks—0
Patchwise Generative ConvNet: Training Energy-Based Models From a Single Natural Image for Internal Learning—0
Posterior Promoted GAN With Distribution Discriminator for Unsupervised Image Synthesis—0
Deep HDR Hallucination for Inverse Tone Mapping—0
Self-Supervised GANs with Label AugmentationCode1
Cascading Modular Network (CAM-Net) for Multimodal Image Synthesis—0
Dynamically Grown Generative Adversarial Networks—0
CT Image Synthesis Using Weakly Supervised Segmentation and Geometric Inter-Label Relations For COVID Image Analysis—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