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 6526–6550 of 6689 papers

TitleStatusHype
Hierarchical Residual Learning Based Vector Quantized Variational Autoencoder for Image Reconstruction and GenerationCode0
Auto-Embedding Generative Adversarial Networks for High Resolution Image SynthesisCode0
CoCoG-2: Controllable generation of visual stimuli for understanding human concept representationCode0
Hierarchical VAE with a Diffusion-based VampPriorCode0
On the Issues of TrueDepth Sensor Data for Computer Vision Tasks Across Different iPad GenerationsCode0
DRMC: A Generalist Model with Dynamic Routing for Multi-Center PET Image SynthesisCode0
High-Dynamic-Range Imaging for Cloud SegmentationCode0
Higher fidelity perceptual image and video compression with a latent conditioned residual denoising diffusion modelCode0
COCO-GAN: Generation by Parts via Conditional CoordinatingCode0
Coordinate-based Texture Inpainting for Pose-Guided Image GenerationCode0
A Bias-Variance-Covariance Decomposition of Kernel Scores for Generative ModelsCode0
Loss-Sensitive Generative Adversarial Networks on Lipschitz DensitiesCode0
DeshuffleGAN: A Self-Supervised GAN to Improve Structure LearningCode0
CoPE: Conditional image generation using Polynomial ExpansionsCode0
Theory and Experiments on Vector Quantized AutoencodersCode0
Self-supervised GAN: Analysis and Improvement with Multi-class Minimax GameCode0
Real-Time Person Image Synthesis Using a Flow Matching ModelCode0
CODE: Confident Ordinary Differential EditingCode0
High-Frequency Anti-DreamBooth: Robust Defense against Personalized Image SynthesisCode0
The relativistic discriminator: a key element missing from standard GANCode0
LR-GAN: Layered Recursive Generative Adversarial Networks for Image GenerationCode0
Self-Supervised GANs via Auxiliary Rotation LossCode0
A Layer-Based Sequential Framework for Scene Generation with GANsCode0
Self-Supervised Image-to-Text and Text-to-Image SynthesisCode0
A User-Friendly Framework for Generating Model-Preferred Prompts in Text-to-Image SynthesisCode0
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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