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 6501–6525 of 6689 papers

TitleStatusHype
Image-Guided Microstructure Optimization using Diffusion Models: Validated with Li-Mn-rich Cathode Precursors—0
Image-guided Neural Object Rendering—0
Image Inpainting Models are Effective Tools for Instruction-guided Image Editing—0
A Two Stage GAN for High Resolution Retinal Image Generation and Segmentation—0
Imagen Video: High Definition Video Generation with Diffusion Models—0
ImageRAG: Dynamic Image Retrieval for Reference-Guided Image Generation—0
Image Regeneration: Evaluating Text-to-Image Model via Generating Identical Image with Multimodal Large Language Models—0
Accelerating Score-based Generative Models for High-Resolution Image Synthesis—0
ZeroAvatar: Zero-shot 3D Avatar Generation from a Single Image—0
Accelerating Mobile Edge Generation (MEG) by Constrained Learning—0
Image Super-Resolution With Deep Variational Autoencoders—0
TiVGAN: Text to Image to Video Generation with Step-by-Step Evolutionary Generator—0
Image Synthesis as a Pretext for Unsupervised Histopathological Diagnosis—0
Image Synthesis-based Late Stage Cancer Augmentation and Semi-Supervised Segmentation for MRI Rectal Cancer Staging—0
Image Synthesis for Data Augmentation in Medical CT using Deep Reinforcement Learning—0
TL-GAN: Improving Traffic Light Recognition via Data Synthesis for Autonomous Driving—0
TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP—0
ToddlerDiffusion: Interactive Structured Image Generation with Cascaded Schrödinger Bridge—0
Image Synthesis via Semantic Composition—0
Toffee: Efficient Million-Scale Dataset Construction for Subject-Driven Text-to-Image Generation—0
Image Synthesis with Class-Aware Semantic Diffusion Models for Surgical Scene Segmentation—0
Image Synthesis with Disentangled Attributes for Chest X-Ray Nodule Augmentation and Detection—0
Attribute Regularized Soft Introspective VAE: Towards Cardiac Attribute Regularization Through MRI Domains—0
Image-to-Image Translation: Methods and Applications—0
Image Transformer—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