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 6526–6550 of 6689 papers

TitleStatusHype
Deep D-bar: Real time Electrical Impedance Tomography Imaging with Deep Neural Networks—0
Challenges in Disentangling Independent Factors of VariationCode0
Artificial Generation of Big Data for Improving Image Classification: A Generative Adversarial Network Approach on SAR DataCode0
Deep Forward and Inverse Perceptual Models for Tracking and Prediction—0
Progressive Growing of GANs for Improved Quality, Stability, and VariationCode2
Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions—0
MR to X-Ray Projection Image Synthesis—0
Generative Adversarial Networks: An OverviewCode0
StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial NetworksCode1
Procedural Modeling and Physically Based Rendering for Synthetic Data Generation in Automotive Applications—0
Generative Adversarial Networks Conditioned by Brain Signals—0
Class-Splitting Generative Adversarial NetworksCode0
Triangle Generative Adversarial NetworksCode0
NiftyNet: a deep-learning platform for medical imagingCode1
Deep Embedding Convolutional Neural Network for Synthesizing CT Image from T1-Weighted MR Image—0
Synthetic Medical Images from Dual Generative Adversarial NetworksCode0
Improved ArtGAN for Conditional Synthesis of Natural Image and ArtworkCode0
Automatic Semantic Style Transfer using Deep Convolutional Neural Networks and Soft MasksCode0
Adversarial nets with perceptual losses for text-to-image synthesis—0
Towards the Automatic Anime Characters Creation with Generative Adversarial NetworksCode0
PixelNN: Example-based Image SynthesisCode0
GANs for Biological Image SynthesisCode0
Material Editing Using a Physically Based Rendering Network—0
Synthesis of Positron Emission Tomography (PET) Images via Multi-channel Generative Adversarial Networks (GANs)Code0
Photographic Image Synthesis with Cascaded Refinement Networks—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