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 4501–4550 of 6689 papers

TitleStatusHype
Transformation GAN for Unsupervised Image Synthesis and Representation Learning—0
MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation—0
Transformation vs Tradition: Artificial General Intelligence (AGI) for Arts and Humanities—0
Measuring Diversity in Co-creative Image Generation—0
Measuring the Success of Diffusion Models at Imitating Human Artists—0
Med-Art: Diffusion Transformer for 2D Medical Text-to-Image Generation—0
MedDiff-FM: A Diffusion-based Foundation Model for Versatile Medical Image Applications—0
MedDiT: A Knowledge-Controlled Diffusion Transformer Framework for Dynamic Medical Image Generation in Virtual Simulated Patient—0
MedEdit: Counterfactual Diffusion-based Image Editing on Brain MRI—0
Are generative models fair? A study of racial bias in dermatological image generation—0
Transformer-based Cross-Modal Recipe Embeddings with Large Batch Training—0
Medical Image Generation using Generative Adversarial Networks—0
Medical Image Segmentation on MRI Images with Missing Modalities: A Review—0
Medical Image Segmentation Using a U-Net type of Architecture—0
Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks—0
Medical Image Synthesis via Fine-Grained Image-Text Alignment and Anatomy-Pathology Prompting—0
Medical Image Synthesis with Context-Aware Generative Adversarial Networks—0
Are Conditional Latent Diffusion Models Effective for Image Restoration?—0
Mediffusion: Joint Diffusion for Self-Explainable Semi-Supervised Classification and Medical Image Generation—0
Transformer-based Generative Adversarial Networks in Computer Vision: A Comprehensive Survey—0
MediSyn: Text-Guided Diffusion Models for Broad Medical 2D and 3D Image Synthesis—0
What Does DALL-E 2 Know About Radiology?—0
Transformer for Times Series: an Application to the S&P500—0
Are conditional GANs explicitly conditional?—0
MedSegDiffNCA: Diffusion Models With Neural Cellular Automata for Skin Lesion Segmentation—0
A Recipe for Scaling up Text-to-Video Generation with Text-free Videos—0
Transformers in Medical Image Analysis: A Review—0
MEGANE: Morphable Eyeglass and Avatar Network—0
MEGAN: Mixture of Experts of Generative Adversarial Networks for Multimodal Image Generation—0
Megapixel Size Image Creation using Generative Adversarial Networks—0
ArcGAN: Generative Adversarial Networks for 3D Architectural Image Generation—0
MelanoGANs: High Resolution Skin Lesion Synthesis with GANs—0
Transforming Image Generation from Scene Graphs—0
Transform the Set: Memory Attentive Generation of Guided and Unguided Image Collages—0
Membership Attacks on Conditional Generative Models Using Image Difficulty—0
Membership Inference Attacks Against Text-to-image Generation Models—0
Membership Inference Attacks on Diffusion Models via Quantile Regression—0
What If We Recaption Billions of Web Images with LLaMA-3?—0
Memories of Forgotten Concepts—0
Memory-Driven Text-to-Image Generation—0
Memory-Efficient 3D High-Resolution Medical Image Synthesis Using CRF-Guided GANs—0
Memory-efficient GAN-based Domain Translation of High Resolution 3D Medical Images—0
Memory Triggers: Unveiling Memorization in Text-To-Image Generative Models through Word-Level Duplication—0
Translate the Facial Regions You Like Using Region-Wise Normalization—0
Translating and Segmenting Multimodal Medical Volumes with Cycle- and Shape-Consistency Generative Adversarial Network—0
Message Passing Multi-Agent GANs—0
MEt3R: Measuring Multi-View Consistency in Generated Images—0
MetaCLUE: Towards Comprehensive Visual Metaphors Research—0
Translation-Enhanced Multilingual Text-to-Image Generation—0
MetaDecorator: Generating Immersive Virtual Tours through Multimodality—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