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 6301–6350 of 6689 papers

TitleStatusHype
Medical Image Super-Resolution Using a Generative Adversarial Network—0
Progressive Augmentation of GANsCode0
TGAN: Deep Tensor Generative Adversarial Nets for Large Image GenerationCode0
Scene Text Synthesis for Efficient and Effective Deep Network Training—0
Virtual Conditional Generative Adversarial NetworksCode0
Generative Adversarial Network with Multi-Branch Discriminator for Cross-Species Image-to-Image Translation—0
Learning Disentangled Representations with Reference-Based Variational Autoencoders—0
Toward Joint Image Generation and Compression using Generative Adversarial Networks—0
Learning Spatial Pyramid Attentive Pooling in Image Synthesis and Image-to-Image Translation—0
Red blood cell image generation for data augmentation using Conditional Generative Adversarial Networks—0
Generative Adversarial Classifier for Handwriting Characters Super-Resolution—0
Using Scene Graph Context to Improve Image Generation—0
CSGAN: Cyclic-Synthesized Generative Adversarial Networks for Image-to-Image TransformationCode0
Adversarial Pseudo Healthy Synthesis Needs Pathology Factorization—0
Preventing Posterior Collapse with delta-VAEs—0
Thinking Outside the Pool: Active Training Image Creation for Relative Attributes—0
FIGR: Few-shot Image Generation with ReptileCode0
Unpaired Pose Guided Human Image GenerationCode0
MAE: Mutual Posterior-Divergence Regularization for Variational AutoEncoders—0
Generating Multiple Objects at Spatially Distinct LocationsCode0
Improving MMD-GAN Training with Repulsive Loss FunctionCode0
Non-Adversarial Image Synthesis with Generative Latent Nearest NeighborsCode0
Generative Models from the perspective of Continual LearningCode0
Unsupervised Meta-learning of Figure-Ground Segmentation via Imitating Visual EffectsCode0
Sequential Attention GAN for Interactive Image Editing—0
Latent Dirichlet Allocation in Generative Adversarial Networks—0
Spatial Fusion GAN for Image Synthesis—0
Unsupervised Image Decomposition in Vector Layers—0
Synthesis of High-Quality Visible Faces from Polarimetric Thermal Faces using Generative Adversarial Networks—0
Adversarial Learning of Semantic Relevance in Text to Image Synthesis—0
Considering Race a Problem of Transfer Learning—0
PIV/BOS Synthetic Image Generation in Variable Density Environments for Error Analysis and Experiment Design—0
Evaluating the Impact of Intensity Normalization on MR Image SynthesisCode0
Visual Object Networks: Image Generation with Disentangled 3D RepresentationCode0
Generating High Fidelity Images with Subscale Pixel Networks and Multidimensional Upscaling—0
Zoom-In-to-Check: Boosting Video Interpolation via Instance-level Discrimination—0
FaceFeat-GAN: a Two-Stage Approach for Identity-Preserving Face Synthesis—0
Decompose to manipulate: Manipulable Object Synthesis in 3D Medical Images with Structured Image Decomposition—0
Disentangling Latent Hands for Image Synthesis and Pose Estimation—0
Multivariate Time Series Imputation with Generative Adversarial Networks—0
Visual Object Networks: Image Generation with Disentangled 3D RepresentationsCode0
Probabilistic Neural Programmed Networks for Scene GenerationCode0
Image-based model parameter optimization using Model-Assisted Generative Adversarial Networks—0
Shape-conditioned Image Generation by Learning Latent Appearance Representation from Unpaired Data—0
Coordinate-based Texture Inpainting for Pose-Guided Image GenerationCode0
Self-Supervised GANs via Auxiliary Rotation LossCode0
Class-Distinct and Class-Mutual Image Generation with GANsCode0
IGNOR: Image-guided Neural Object Rendering—0
Spatially Controllable Image Synthesis with Internal Representation CollagingCode0
GAN Dissection: Visualizing and Understanding Generative Adversarial NetworksCode0
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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