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 5101–5150 of 6689 papers

TitleStatusHype
Arbitrary Style Guidance for Enhanced Diffusion-Based Text-to-Image Generation—0
Extreme Generative Image Compression by Learning Text Embedding from Diffusion Models—0
Controllable GAN Synthesis Using Non-Rigid Structure-from-Motion—0
Structural constrained virtual histology staining for human coronary imaging using deep learning—0
Physically-Based Face Rendering for NIR-VIS Face Recognition—0
HumanDiffusion: a Coarse-to-Fine Alignment Diffusion Framework for Controllable Text-Driven Person Image Generation—0
SSGVS: Semantic Scene Graph-to-Video Synthesis—0
Self-conditioned Embedding Diffusion for Text Generation—0
Few-shot Image Generation with Diffusion ModelsCode0
Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large ScaleCode0
Evaluating a Synthetic Image Dataset Generated with Stable Diffusion—0
Book Cover Synthesis from the Summary—0
Generation of Anonymous Chest Radiographs Using Latent Diffusion Models for Training Thoracic Abnormality Classification Systems—0
CLIP-Sculptor: Zero-Shot Generation of High-Fidelity and Diverse Shapes from Natural Language—0
Spot the fake lungs: Generating Synthetic Medical Images using Neural Diffusion Models—0
Synthetic ID Card Image Generation for Improving Presentation Attack Detection—0
gCoRF: Generative Compositional Radiance Fields—0
Recursive Reasoning in Minimax Games: A Level k Gradient Play MethodCode0
UPainting: Unified Text-to-Image Diffusion Generation with Cross-modal Guidance—0
Latent Space is Feature Space: Regularization Term for GANs Training on Limited DatasetCode0
ERNIE-ViLG 2.0: Improving Text-to-Image Diffusion Model with Knowledge-Enhanced Mixture-of-Denoising-ExpertsCode0
Accelerating Diffusion Models via Pre-segmentation Diffusion Sampling for Medical Image Segmentation—0
A Generic Shared Attention Mechanism for Various Backbone Neural Networks—0
ScoreMix: A Scalable Augmentation Strategy for Training GANs with Limited Data—0
Few-shot Image Generation via Masked DiscriminationCode0
SSD: Towards Better Text-Image Consistency Metric in Text-to-Image GenerationCode0
Towards Practicality of Sketch-Based Visual Understanding—0
Lafite2: Few-shot Text-to-Image Generation—0
Learning Latent Structural Causal Models—0
Photo-realistic Neural Domain Randomization—0
Diffusion Motion: Generate Text-Guided 3D Human Motion by Diffusion Model—0
A Visual Tour Of Current Challenges In Multimodal Language Models—0
Instance-Aware Image Completion—0
Efficient Hair Style Transfer with Generative Adversarial Networks—0
Z-LaVI: Zero-Shot Language Solver Fueled by Visual ImaginationCode0
Stochastic Adaptive Activation FunctionCode0
Composing Ensembles of Pre-trained Models via Iterative Consensus—0
Backdoor Attack and Defense in Federated Generative Adversarial Network-based Medical Image Synthesis—0
Swinv2-Imagen: Hierarchical Vision Transformer Diffusion Models for Text-to-Image Generation—0
Improving GANs with a Feature Cycling Generator—0
From Play to Policy: Conditional Behavior Generation from Uncurated Robot Data—0
A Patch-Based Algorithm for Diverse and High Fidelity Single Image GenerationCode0
DiffGAR: Model-Agnostic Restoration from Generative Artifacts Using Image-to-Image Diffusion Models—0
Character-Centric Story Visualization via Visual Planning and Token AlignmentCode0
LAION-5B: An open large-scale dataset for training next generation image-text modelsCode0
DE-FAKE: Detection and Attribution of Fake Images Generated by Text-to-Image Generation Models—0
BLADERUNNER: Rapid Countermeasure for Synthetic (AI-Generated) StyleGAN FacesCode0
FontTransformer: Few-shot High-resolution Chinese Glyph Image Synthesis via Stacked Transformers—0
Style-Guided Inference of Transformer for High-resolution Image Synthesis—0
Controllable Radiance Fields for Dynamic Face Synthesis—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