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 4551–4600 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
Semantic Image Synthesis for Abdominal CT—0
Variational Lossy Autoencoder—0
Adversarial Generation of Natural Language—0
Variational Potential Flow: A Novel Probabilistic Framework for Energy-Based Generative Modelling—0
Semantic Image Synthesis with Semantically Coupled VQ-Model—0
Variational Quantum Circuits Enhanced Generative Adversarial Network—0
Semantic Image Synthesis with Unconditional Generator—0
Semantic Image Translation for Repairing the Texture Defects of Building Models—0
Semantic Jitter: Dense Supervision for Visual Comparisons via Synthetic Images—0
Joint fMRI Decoding and Encoding with Latent Embedding Alignment—0
Semantic Packet Aggregation and Repeated Transmission for Text-to-Image Generation—0
Variational Schrödinger Diffusion Models—0
Variational Schrödinger Momentum Diffusion—0
Semantic RGB-D Image Synthesis—0
Variation-Aware Semantic Image Synthesis—0
Semantics Disentangling for Text-to-Image Generation—0
Semantics-Enhanced Adversarial Nets for Text-to-Image Synthesis—0
DRAGON: A Large-Scale Dataset of Realistic Images Generated by Diffusion Models—0
CompAlign: Improving Compositional Text-to-Image Generation with a Complex Benchmark and Fine-Grained Feedback—0
VDG: Vision-Only Dynamic Gaussian for Driving Simulation—0
SemDP: Semantic-level Differential Privacy Protection for Face Datasets—0
Semi-Supervised Adaptation of Diffusion Models for Handwritten Text Generation—0
Semi-supervised FusedGAN for Conditional Image Generation—0
Semi-supervised learning based on generative adversarial network: a comparison between good GAN and bad GAN approach—0
Semi-Supervised Single-Stage Controllable GANs for Conditional Fine-Grained Image Generation—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