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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5751–5800 of 6689 papers

TitleStatusHype
GuidedStyle: Attribute Knowledge Guided Style Manipulation for Semantic Face Editing—0
Self-supervised monocular depth estimation from oblique UAV videosCode0
Three Dimensional MR Image Synthesis with Progressive Generative Adversarial Networks—0
Infinite Nature: Perpetual View Generation of Natural Scenes from a Single ImageCode0
Combating Mode Collapse in GAN training: An Empirical Analysis using Hessian Eigenvalues—0
Unlabeled Data Guided Semi-supervised Histopathology Image Segmentation—0
Latent Space Conditioning on Generative Adversarial Networks—0
Sat2Vid: Street-view Panoramic Video Synthesis from a Single Satellite Image—0
Learning Energy-Based Models With Adversarial TrainingCode0
Enhance Convolutional Neural Networks with Noise Incentive Block—0
Learning Portrait Style RepresentationsCode0
Data InStance Prior (DISP) in Generative Adversarial Networks—0
GMM-Based Generative Adversarial Encoder Learning—0
Conditional Generation of Medical Images via Disentangled Adversarial Inference—0
MPG: A Multi-ingredient Pizza Image Generator with Conditional StyleGANsCode0
Few-shot Image Generation with Elastic Weight Consolidation—0
Flow-based Deformation Guidance for Unpaired Multi-Contrast MRI Image-to-Image Translation—0
A Framework and Dataset for Abstract Art Generation via CalligraphyGAN—0
Cross-Modal Retrieval and Synthesis (X-MRS): Closing the Modality Gap in Shared Representation Learning—0
TR at SemEval-2020 Task 4: Exploring the Limits of Language-model-based Common Sense Validation—0
Neural FFTs for Universal Texture Image Synthesis—0
CycleGAN without checkerboard artifacts for counter-forensics of fake-image detection—0
CloTH-VTON: Clothing Three-dimensional reconstruction for Hybrid image-based Virtual Try-ON—0
Learning geometry-image representation for 3D point cloud generation—0
Tractable loss function and color image generation of multinary restricted Boltzmann machine—0
Adaptive Multiplane Image Generation from a Single Internet Picture—0
Multiclass non-Adversarial Image Synthesis, with Application to Classification from Very Small Sample—0
Augmentation-Interpolative AutoEncoders for Unsupervised Few-Shot Image Generation—0
Improving Augmentation and Evaluation Schemes for Semantic Image Synthesis—0
Generative Adversarial Stacked Autoencoders—0
Dual Contradistinctive Generative Autoencoder—0
Style Intervention: How to Achieve Spatial Disentanglement with Style-based Generators?—0
Cycle-Consistent Generative Rendering for 2D-3D Modality TranslationCode0
Survey2Survey: A deep learning generative model approach for cross-survey image mapping—0
Learning from THEODORE: A Synthetic Omnidirectional Top-View Indoor Dataset for Deep Transfer Learning—0
(f,Γ)-Divergences: Interpolating between f-Divergences and Integral Probability Metrics—0
Using GANs to Synthesise Minimum Training Data for Deepfake Generation—0
Two-Stream Appearance Transfer Network for Person Image Generation—0
Blind Motion Deblurring through SinGAN Architecture—0
DeepCFL: Deep Contextual Features Learning from a Single Image—0
DTGAN: Dual Attention Generative Adversarial Networks for Text-to-Image Generation—0
Augmenting Images for ASR and TTS through Single-loop and Dual-loop Multimodal Chain Framework—0
Exploring DeshuffleGANs in Self-Supervised Generative Adversarial NetworksCode0
Creating cloud-free satellite imagery from image time series with deep learning—0
BLT: Balancing Long-Tailed Datasets with Adversarially-Perturbed ImagesCode0
Maximum a posteriori signal recovery for optical coherence tomography angiography image generation and denoising—0
Leveraging Visual Question Answering to Improve Text-to-Image Synthesis—0
Geometrically Matched Multi-source Microscopic Image Synthesis Using Bidirectional Adversarial Networks—0
LEAD: Min-Max Optimization from a Physical PerspectiveCode0
Autoregressive Score Matching—0
Show:102550
← PrevPage 116 of 134Next →
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