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 5276–5300 of 6689 papers

TitleStatusHype
Recent Progress of Face Image Synthesis—0
Recommendation with Generative Models—0
Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding—0
Reconstructing the Noise Manifold for Image Denoising—0
Reconstructing the Noise Variance Manifold for Image Denoising—0
Unsupervised evaluation of GAN sample quality: Introducing the TTJac Score—0
Towards Device Efficient Conditional Image Generation—0
Unsupervised Generative 3D Shape Learning from Natural Images—0
ReCo: Region-Controlled Text-to-Image Generation—0
Recovering Global Data Distribution Locally in Federated Learning—0
AIwriting: Relations Between Image Generation and Digital Writing—0
RectifiedHR: Enable Efficient High-Resolution Image Generation via Energy Rectification—0
Red blood cell image generation for data augmentation using Conditional Generative Adversarial Networks—0
REDEditing: Relationship-Driven Precise Backdoor Poisoning on Text-to-Image Diffusion Models—0
Unsupervised High-Fidelity Facial Texture Generation and Reconstruction—0
Redes Generativas Adversarias (GAN) Fundamentos Teóricos y Aplicaciones—0
Re-designing cities with conditional adversarial networks—0
Unsupervised Histopathology Image Synthesis—0
Model Collapse in the Self-Consuming Chain of Diffusion Finetuning: A Novel Perspective from Quantitative Trait Modeling—0
ReDirTrans: Latent-to-Latent Translation for Gaze and Head Redirection—0
ReDistill: Residual Encoded Distillation for Peak Memory Reduction—0
Red-Teaming the Stable Diffusion Safety Filter—0
Unsupervised Image-generation Enhanced Adaptation for Object Detection in Thermal images—0
ReelFramer: Human-AI Co-Creation for News-to-Video Translation—0
Interactive Image Generation Using Scene Graphs—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