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 6126–6150 of 6689 papers

TitleStatusHype
Generation and Simulation of Yeast Microscopy Imagery with Deep Learning—0
Generation of Anonymous Chest Radiographs Using Latent Diffusion Models for Training Thoracic Abnormality Classification Systems—0
Generation of Synthetic Images for Pedestrian Detection Using a Sequence of GANs—0
Generative Adversarial Classifier for Handwriting Characters Super-Resolution—0
Generative Adversarial Data Programming—0
Generative Adversarial Image Synthesis with Decision Tree Latent Controller—0
Generative Adversarial Network Architectures For Image Synthesis Using Capsule Networks—0
ZeST-NeRF: Using temporal aggregation for Zero-Shot Temporal NeRFs—0
Generative Adversarial Networks and Adversarial Autoencoders: Tutorial and Survey—0
Generative Adversarial Networks and Other Generative Models—0
Training Generative Adversarial Networks via stochastic Nash games—0
Generative Adversarial Networks Bridging Art and Machine Intelligence—0
Generative Adversarial Networks Conditioned by Brain Signals—0
Generative Adversarial Networks for Brain Images Synthesis: A Review—0
Generative adversarial networks for data-scarce spectral applications—0
Generative Adversarial Networks for MR-CT Deformable Image Registration—0
Generative Adversarial Networks in finance: an overview—0
Generative Adversarial Networks with Limited Data: A Survey and Benchmarking—0
Generative Adversarial Network with Multi-Branch Discriminator for Cross-Species Image-to-Image Translation—0
Generative Adversarial Stacked Autoencoders—0
Texture Image Synthesis Using Spatial GAN Based on Vision Transformers—0
Generative Adversarial Training for MRA Image Synthesis Using Multi-Contrast MRI—0
BADM: Batch ADMM for Deep Learning—0
Generative AI and Process Systems Engineering: The Next Frontier—0
Generative AI and the History of Architecture—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