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 5151–5175 of 6689 papers

TitleStatusHype
Deep HDR Hallucination for Inverse Tone Mapping—0
Deep Human-guided Conditional Variational Generative Modeling for Automated Urban Planning—0
StrokeCoder: Path-Based Image Generation from Single Examples using Transformers—0
Deep Image Synthesis from Intuitive User Input: A Review and Perspectives—0
Deep Learning Approach for Hyperspectral Image Demosaicking, Spectral Correction and High-resolution RGB Reconstruction—0
Deep Learning based Multi-modal Computing with Feature Disentanglement for MRI Image Synthesis—0
Deep Learning for Chest X-ray Analysis: A Survey—0
Deeply Supervised Flow-Based Generative Models—0
Deep MMD Gradient Flow without adversarial training—0
Deep neural network techniques for monaural speech enhancement: state of the art analysis—0
deepNIR: Datasets for generating synthetic NIR images and improved fruit detection system using deep learning techniques—0
Deep Nonparametric Convexified Filtering for Computational Photography, Image Synthesis and Adversarial Defense—0
Structural constrained virtual histology staining for human coronary imaging using deep learning—0
Deep OCT Angiography Image Generation for Motion Artifact Suppression—0
Can Generative Geospatial Diffusion Models Excel as Discriminative Geospatial Foundation Models?—0
Structurally aware bidirectional unpaired image to image translation between CT and MR—0
DeepPortraitDrawing: Generating Human Body Images from Freehand Sketches—0
deepSELF: An Open Source Deep Self End-to-End Learning Framework—0
Deep Shading: Convolutional Neural Networks for Screen-Space Shading—0
Structure-aware Person Image Generation with Pose Decomposition and Semantic Correlation—0
Deep Sinogram Completion with Image Prior for Metal Artifact Reduction in CT Images—0
Structured GANs—0
Deep super resolution crack network (SrcNet) for improving computer vision–based automated crack detectability in in situ bridges—0
Structured Prediction using cGANs with Fusion Discriminator—0
DeepWarp: Photorealistic Image Resynthesis for Gaze Manipulation—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