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 5651–5700 of 6689 papers

TitleStatusHype
TransferI2I: Transfer Learning for Image-to-Image Translation from Small Datasets—0
PassFlow: Guessing Passwords with Generative Flows—0
Multiscale Invertible Generative Networks for High-Dimensional Bayesian Inference—0
Directional GAN: A Novel Conditioning Strategy for Generative Networks—0
GANs for Medical Image Synthesis: An Empirical Study—0
CASIA-Face-Africa: A Large-scale African Face Image Database—0
Deep Learning based Multi-modal Computing with Feature Disentanglement for MRI Image Synthesis—0
Fast Adaptive Meta-Learning for Few-Shot Image GenerationCode0
Perceptual Gradient NetworksCode0
TREND: Truncated Generalized Normal Density Estimation of Inception Embeddings for GAN Evaluation—0
Pose-driven Attention-guided Image Generation for Person Re-Identification—0
Image Synthesis as a Pretext for Unsupervised Histopathological Diagnosis—0
CAGAN: Text-To-Image Generation with Combined Attention GANs—0
Eccentric Regularization: Minimizing Hyperspherical Energy without explicit projection—0
Principal Component Density Estimation for Scenario Generation Using Normalizing Flows—0
Imaginative Walks: Generative Random Walk Deviation Loss for Improved Unseen Learning RepresentationCode0
Coarse-to-Fine Searching for Efficient Generative Adversarial Networks—0
StEP: Style-based Encoder Pre-training for Multi-modal Image Synthesis—0
VTGAN: Semi-supervised Retinal Image Synthesis and Disease Prediction using Vision TransformersCode0
IMAGINE: Image Synthesis by Image-Guided Model Inversion—0
Automatic Correction of Internal Units in Generative Neural Networks—0
Multi-View Image-to-Image Translation Supervised by 3D PoseCode0
CoPE: Conditional image generation using Polynomial ExpansionsCode0
SIGAN: A Novel Image Generation Method for Solar Cell Defect Segmentation and Augmentation—0
Pose Invariant Person Re-Identification using Robust Pose-transformation GANCode0
Re-designing cities with conditional adversarial networks—0
Noise Estimation for Generative Diffusion Models—0
Exploiting Relationship for Complex-scene Image Generation—0
Improved Image Generation via Sparse Modeling—0
Multi-Class Multi-Instance Count Conditioned Adversarial Image GenerationCode0
CAMPARI: Camera-Aware Decomposed Generative Neural Radiance Fields—0
Identity-Aware CycleGAN for Face Photo-Sketch Synthesis and Recognition—0
PeaceGAN: A GAN-based Multi-Task Learning Method for SAR Target Image Generation with a Pose Estimator and an Auxiliary Classifier—0
Photoacoustic image synthesis with generative adversarial networks—0
LatentKeypointGAN: Controlling Images via Latent KeypointsCode0
Modeling the Nonsmoothness of Modern Neural Networks—0
AttrLostGAN: Attribute Controlled Image Synthesis from Reconfigurable Layout and StyleCode0
SETGAN: Scale and Energy Trade-off GANs for Image Applications on Mobile Platforms—0
Brain Image Synthesis with Unsupervised Multivariate Canonical CSC_4Net—0
Generation and Simulation of Yeast Microscopy Imagery with Deep Learning—0
Efficient Subsampling of Realistic Images From GANs Conditional on a Class or a Continuous VariableCode0
Carton dataset synthesis method for domain shift based on foreground texture decoupling and replacementCode0
Generic Perceptual Loss for Modeling Structured Output Dependencies—0
Image Synthesis for Data Augmentation in Medical CT using Deep Reinforcement Learning—0
Bias-Free FedGAN: A Federated Approach to Generate Bias-Free Datasets—0
Deep Consensus Learning—0
Assessment of image generation by quantum annealer—0
Deep Learning for Chest X-ray Analysis: A Survey—0
Fine-grained MRI Reconstruction using Attentive Selection Generative Adversarial NetworksCode0
Unsupervised Image Transformation Learning via 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