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 4551–4575 of 6689 papers

TitleStatusHype
Variational Capsules for Image Analysis and Synthesis—0
Self Sparse Generative Adversarial Networks—0
Self-Supervised 3D Human Pose Estimation via Part Guided Novel Image Synthesis—0
Self-Supervised 3D Mesh Reconstruction From Single Images—0
Self-supervised Correlation Mining Network for Person Image Generation—0
Self-supervised Deformation Modeling for Facial Expression Editing—0
X-Transfer: A Transfer Learning-Based Framework for GAN-Generated Fake Image Detection—0
Self-supervised Multi-task Learning Framework for Safety and Health-Oriented Connected Driving Environment Perception using Onboard Camera—0
Variational Distribution Learning for Unsupervised Text-to-Image Generation—0
Self-Supervised Text Erasing with Controllable Image Synthesis—0
Sell It Before You Make It: Revolutionizing E-Commerce with Personalized AI-Generated Items—0
SeLoRA: Self-Expanding Low-Rank Adaptation of Latent Diffusion Model for Medical Image Synthesis—0
Conditional Diffusion on Web-Scale Image Pairs leads to Diverse Image Variations—0
Semantically Invariant Text-to-Image Generation—0
Variational Domain Adaptation—0
Variational f-divergence Minimization—0
Adversarial Generation of Training Examples: Applications to Moving Vehicle License Plate Recognition—0
Semantic Draw Engineering for Text-to-Image Creation—0
Variational learning across domains with triplet information—0
Semantic Editing On Segmentation Map Via Multi-Expansion Loss—0
Semantic-Guided Generative Image Augmentation Method with Diffusion Models for Image Classification—0
Semantic Hierarchy Emerges in the Deep Generative Representations for Scene Synthesis—0
SemanticHuman-HD: High-Resolution Semantic Disentangled 3D Human Generation—0
Semantic Image Inversion and Editing using Rectified Stochastic Differential Equations—0
Variational learning across domains with triplet information—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