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 6401–6450 of 6689 papers

TitleStatusHype
Geometric Image Synthesis—0
Neural Rendering and Reenactment of Human Actor Videos—0
Mapping Instructions to Actions in 3D Environments with Visual Goal PredictionCode0
Open Set Learning with Counterfactual Images—0
On Learning 3D Face Morphable Model from In-the-wild ImagesCode0
TreeGAN: Syntax-Aware Sequence Generation with Generative Adversarial Networks—0
Escaping from Collapsing Modes in a Constrained SpaceCode0
Learning Hierarchical Semantic Image Manipulation through Structured RepresentationsCode0
Text-to-image Synthesis via Symmetrical Distillation Networks—0
GridFace: Face Rectification via Learning Local Homography Transformations—0
Towards Audio to Scene Image Synthesis using Generative Adversarial Network—0
Unsupervised learning for cross-domain medical image synthesis using deformation invariant cycle consistency networks—0
Paired 3D Model Generation with Conditional Generative Adversarial NetworksCode0
Diverse Conditional Image Generation by Stochastic Regression with Latent Drop-Out CodesCode0
Rob-GAN: Generator, Discriminator, and Adversarial AttackerCode0
Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks—0
Improved Training with Curriculum GANs—0
Generic Camera Attribute Control using Bayesian Optimization—0
Generative Adversarial Networks for MR-CT Deformable Image Registration—0
IntroVAE: Introspective Variational Autoencoders for Photographic Image SynthesisCode0
Variational Capsules for Image Analysis and Synthesis—0
Generative Adversarial Networks with Decoder-Encoder Output NoiseCode0
High-Resolution Mammogram Synthesis using Progressive Generative Adversarial Networks—0
Verisimilar Image Synthesis for Accurate Detection and Recognition of Texts in Scenes—0
Pioneer Networks: Progressively Growing Generative AutoencoderCode0
Vehicle Image Generation Going Well with The Surroundings—0
The relativistic discriminator: a key element missing from standard GANCode0
SynNet: Structure-Preserving Fully Convolutional Networks for Medical Image Synthesis—0
Deep Generative Models with Learnable Knowledge Constraints—0
Variational learning across domains with triplet information—0
Unsupervised Learning of Object Landmarks through Conditional Image GenerationCode0
The Neural Painter: Multi-Turn Image Generation—0
Generative Adversarial Network Architectures For Image Synthesis Using Capsule Networks—0
CapsGAN: Using Dynamic Routing for Generative Adversarial NetworksCode0
Multistage Adversarial Losses for Pose-Based Human Image Synthesis—0
Generative Modeling by Inclusive Neural Random Fields with Applications in Image Generation and Anomaly DetectionCode0
Joint Pose and Expression Modeling for Facial Expression Recognition—0
Whitening and Coloring batch transform for GANsCode0
Human Appearance Transfer—0
Multi-Task Adversarial Network for Disentangled Feature Learning—0
A Hierarchical Generative Model for Eye Image Synthesis and Eye Gaze Estimation—0
On GANs and GMMsCode0
On gradient regularizers for MMD GANsCode0
Theory and Experiments on Vector Quantized AutoencodersCode0
Deep Generative Models for Distribution-Preserving Lossy CompressionCode0
Generative Adversarial Image Synthesis with Decision Tree Latent Controller—0
Cross Domain Image Generation through Latent Space Exploration with Adversarial Loss—0
Distribution Matching Losses Can Hallucinate Features in Medical Image TranslationCode0
Robust Conditional Generative Adversarial NetworksCode0
Anime Style Space Exploration Using Metric Learning and Generative Adversarial 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