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 4201–4250 of 6689 papers

TitleStatusHype
Labeled Graph Generative Adversarial Networks—0
Label-free Neural Semantic Image Synthesis—0
Lafite2: Few-shot Text-to-Image Generation—0
Investigating Object Compositionality in Generative Adversarial Networks—0
A Survey and Taxonomy of Adversarial Neural Networks for Text-to-Image Synthesis—0
A Study of Posterior Stability for Time-Series Latent Diffusion—0
A Streaming Volumetric Image Generation Framework for Development and Evaluation of Out-of-Core Methods—0
Lane Detection System for Driver Assistance in Vehicles—0
Language-agnostic Semantic Consistent Text-to-Image Generation—0
Language-Guided Image Tokenization for Generation—0
Language-Guided Trajectory Traversal in Disentangled Stable Diffusion Latent Space for Factorized Medical Image Generation—0
Language-Informed Hyperspectral Image Synthesis for Imbalanced-Small Sample Classification via Semi-Supervised Conditional Diffusion Model—0
A-STAR: Test-time Attention Segregation and Retention for Text-to-image Synthesis—0
Language Modeling with Generative AdversarialNetworks—0
Language-Oriented Communication with Semantic Coding and Knowledge Distillation for Text-to-Image Generation—0
LaPIG: Cross-Modal Generation of Paired Thermal and Visible Facial Images—0
LAPIG: Language Guided Projector Image Generation with Surface Adaptation and Stylization—0
Laplacian Patch-Based Image Synthesis—0
LapLoss: Laplacian Pyramid-based Multiscale loss for Image Translation—0
A SSIM Guided cGAN Architecture For Clinically Driven Generative Image Synthesis of Multiplexed Spatial Proteomics Channels—0
Large Language Models Suffer From Their Own Output: An Analysis of the Self-Consuming Training Loop—0
Assessment of image generation by quantum annealer—0
Towards Scalable Topological Regularizers—0
Wavelet-based Variational Autoencoders for High-Resolution Image Generation—0
Large-Scale Public Data Improves Differentially Private Image Generation Quality—0
Large-scale Reinforcement Learning for Diffusion Models—0
LASER: Tuning-Free LLM-Driven Attention Control for Efficient Text-conditioned Image-to-Animation—0
Assessing the capacity of a denoising diffusion probabilistic model to reproduce spatial context—0
Human Synthesis and Scene Compositing—0
Towards the Detection of AI-Synthesized Human Face Images—0
Latent diffusion models for parameterization and data assimilation of facies-based geomodels—0
Latent Diffusion Models for Structural Component Design—0
Latent Dirichlet Allocation in Generative Adversarial Networks—0
Latent Drifting in Diffusion Models for Counterfactual Medical Image Synthesis—0
Assessing the ability of generative adversarial networks to learn canonical medical image statistics—0
Assessing Robustness via Score-Based Adversarial Image Generation—0
Latent Pollution Model: The Hidden Carbon Footprint in 3D Image Synthesis—0
LatentQGAN: A Hybrid QGAN with Classical Convolutional Autoencoder—0
Latent Space Bayesian Optimization with Latent Data Augmentation for Enhanced Exploration—0
Latent Schrodinger Bridge: Prompting Latent Diffusion for Fast Unpaired Image-to-Image Translation—0
Latent-Shift: Latent Diffusion with Temporal Shift for Efficient Text-to-Video Generation—0
Latent Space Conditioning on Generative Adversarial Networks—0
Latent Space Consistency for Sparse-View CT Reconstruction—0
Latent Space Disentanglement in Diffusion Transformers Enables Precise Zero-shot Semantic Editing—0
Towards the Unification of Generative and Discriminative Visual Foundation Model: A Survey—0
Assessing Open-world Forgetting in Generative Image Model Customization—0
Latent Wavelet Diffusion: Enabling 4K Image Synthesis for Free—0
Lateralization LoRA: Interleaved Instruction Tuning with Modality-Specialized Adaptations—0
LatexBlend: Scaling Multi-concept Customized Generation with Latent Textual Blending—0
LAW-Diffusion: Complex Scene Generation by Diffusion with Layouts—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