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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4401–4450 of 6689 papers

TitleStatusHype
PRISM: Progressive Restoration for Scene Graph-based Image Manipulation—0
GPT-4V(ision) as a Generalist Evaluator for Vision-Language Tasks—0
Towards High-quality HDR Deghosting with Conditional Diffusion Models—0
Flooding Regularization for Stable Training of Generative Adversarial Networks—0
Mukh-Oboyob: Stable Diffusion and BanglaBERT enhanced Bangla Text-to-Face SynthesisCode0
Optimal Budgeted Rejection Sampling for Generative Models—0
The Missing U for Efficient Diffusion Models—0
Diversity and Diffusion: Observations on Synthetic Image Distributions with Stable Diffusion—0
CustomNet: Zero-shot Object Customization with Variable-Viewpoints in Text-to-Image Diffusion Models—0
Transformation vs Tradition: Artificial General Intelligence (AGI) for Arts and Humanities—0
IterInv: Iterative Inversion for Pixel-Level T2I ModelsCode0
Davidsonian Scene Graph: Improving Reliability in Fine-grained Evaluation for Text-to-Image Generation—0
DiffS2UT: A Semantic Preserving Diffusion Model for Textless Direct Speech-to-Speech Translation—0
Three-dimensional Bone Image Synthesis with Generative Adversarial Networks—0
CADS: Unleashing the Diversity of Diffusion Models through Condition-Annealed Sampling—0
Exploring the Potential of Generative AI for the World Wide Web—0
Local Statistics for Generative Image Detection—0
A Picture is Worth a Thousand Words: Principled Recaptioning Improves Image Generation—0
UAV-Sim: NeRF-based Synthetic Data Generation for UAV-based Perception—0
Interferometric Neural NetworksCode0
Complex Image Generation SwinTransformer Network for Audio DenoisingCode0
Learning Low-Rank Latent Spaces with Simple Deterministic Autoencoder: Theoretical and Empirical InsightsCode0
RePoseDM: Recurrent Pose Alignment and Gradient Guidance for Pose Guided Image Synthesis—0
DEsignBench: Exploring and Benchmarking DALL-E 3 for Imagining Visual DesignCode0
Bridging the Gap between Synthetic and Authentic Images for Multimodal Machine TranslationCode0
Localizing and Editing Knowledge in Text-to-Image Generative Models—0
PrivacyGAN: robust generative image privacy—0
STANLEY: Stochastic Gradient Anisotropic Langevin Dynamics for Learning Energy-Based Models—0
Conditional Generative Modeling for Images, 3D Animations, and Video—0
Diverse Diffusion: Enhancing Image Diversity in Text-to-Image Generation—0
BiomedJourney: Counterfactual Biomedical Image Generation by Instruction-Learning from Multimodal Patient Journeys—0
ViPE: Visualise Pretty-much EverythingCode0
Enhancing ML model accuracy for Digital VLSI circuits using diffusion models: A study on synthetic data generation—0
Unified High-binding Watermark for Unconditional Image Generation Models—0
Discovery and Expansion of New Domains within Diffusion Models—0
Hypernymy Understanding Evaluation of Text-to-Image Models via WordNet HierarchyCode0
CopyScope: Model-level Copyright Infringement Quantification in the Diffusion Workflow—0
R&B: Region and Boundary Aware Zero-shot Grounded Text-to-image Generation—0
HyperHuman: Hyper-Realistic Human Generation with Latent Structural Diffusion—0
Unmasking Bias in Diffusion Model TrainingCode0
Neural Diffusion Models—0
SingleInsert: Inserting New Concepts from a Single Image into Text-to-Image Models for Flexible Editing—0
Distance Weighted Trans Network for Image Completion—0
Multi-Concept T2I-Zero: Tweaking Only The Text Embeddings and Nothing Else—0
JointNet: Extending Text-to-Image Diffusion for Dense Distribution Modeling—0
Improving Compositional Text-to-image Generation with Large Vision-Language Models—0
Learning Stackable and Skippable LEGO Bricks for Efficient, Reconfigurable, and Variable-Resolution Diffusion ModelingCode0
Locality-Aware Generalizable Implicit Neural Representation—0
Perceptual Artifacts Localization for Image Synthesis Tasks—0
A Bias-Variance-Covariance Decomposition of Kernel Scores for Generative ModelsCode0
Show:102550
← PrevPage 89 of 134Next →
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