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 4726–4750 of 6689 papers

TitleStatusHype
Computerized Tomography Pulmonary Angiography Image Simulation using Cycle Generative Adversarial Network from Chest CT imaging in Pulmonary Embolism Patients—0
BBDM: Image-to-image Translation with Brownian Bridge Diffusion ModelsCode2
Conditional Vector Graphics Generation for Music Cover ImagesCode1
StyLandGAN: A StyleGAN based Landscape Image Synthesis using Depth-map—0
The Effectiveness of Temporal Dependency in Deepfake Video Detection—0
D3T-GAN: Data-Dependent Domain Transfer GANs for Few-shot Image Generation—0
Ray Priors through Reprojection: Improving Neural Radiance Fields for Novel View Extrapolation—0
Transformer-based Cross-Modal Recipe Embeddings with Large Batch Training—0
A Closer Look at Few-shot Image Generation—0
On Conditioning the Input Noise for Controlled Image Generation with Diffusion Models—0
CCMB: A Large-scale Chinese Cross-modal BenchmarkCode1
High-Resolution UAV Image Generation for Sorghum Panicle Detection—0
GAN-Based Multi-View Video Coding with Spatio-Temporal EPI Reconstruction—0
Text to artistic image generationCode0
DeepPortraitDrawing: Generating Human Body Images from Freehand Sketches—0
BiOcularGAN: Bimodal Synthesis and Annotation of Ocular ImagesCode0
Subspace Diffusion Generative ModelsCode1
A Comprehensive Survey of Image Augmentation Techniques for Deep Learning—0
DANBO: Disentangled Articulated Neural Body Representations via Graph Neural Networks—0
Seeding Diversity into AI Art—0
Augmentation Techniques Analysis with Removal of Class Imbalance Using PyTorch for Intel Scene Dataset—0
Augmented Balanced Image Dataset Generator Using AugStatic LibraryCode0
Improving Model Performance and Removing the Class Imbalance Problem Using Augmentation—0
AugStatic - A Light-Weight Image Augmentation LibraryCode0
Deep PCB To COCO ConvertorCode2
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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