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 3526–3550 of 6689 papers

TitleStatusHype
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
MetaEarth: A Generative Foundation Model for Global-Scale Remote Sensing Image Generation—0
MetaHead: An Engine to Create Realistic Digital Head—0
TR at SemEval-2020 Task 4: Exploring the Limits of Language-model-based Common Sense Validation—0
Meta-Learning for Speeding Up Large Model Inference in Decentralized Environments—0
Meta-Learning Divergences of Variational Inference—0
MetalGAN: Multi-Domain Label-Less Image Synthesis Using cGANs and Meta-Learning—0
Method and Software Tool for Generating Artificial Databases of Biomedical Images Based on Deep Neural Networks—0
What Lurks Within? Concept Auditing for Shared Diffusion Models at Scale—0
Metrics to Quantify Global Consistency in Synthetic Medical Images—0
TR-DQ: Time-Rotation Diffusion Quantization—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