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 6001–6050 of 6689 papers

TitleStatusHype
Triple Generative Adversarial NetworksCode0
Adversarial symmetric GANs: bridging adversarial samples and adversarial networksCode0
Neural Design Network: Graphic Layout Generation with Constraints—0
CPGAN: Full-Spectrum Content-Parsing Generative Adversarial Networks for Text-to-Image SynthesisCode0
Learning to Segment Brain Anatomy from 2D Ultrasound with Less Data—0
Jointly Trained Image and Video Generation using Residual Vectors—0
Image Processing Using Multi-Code GAN PriorCode0
Region and Object based Panoptic Image Synthesis through Conditional GANs—0
An Efficient Explorative Sampling Considering the Generative Boundaries of Deep Generative Neural Networks—0
Unified Generative Adversarial Networks for Controllable Image-to-Image TranslationCode0
Towards Unsupervised Learning of Generative Models for 3D Controllable Image SynthesisCode0
Neural Voxel Renderer: Learning an Accurate and Controllable Rendering ToolCode0
InfoCNF: An Efficient Conditional Continuous Normalizing Flow with Adaptive Solvers—0
Learning Disentangled Representations via Mutual Information EstimationCode0
Amora: Black-box Adversarial Morphing Attack—0
Neural Wireframe Renderer: Learning Wireframe to Image TranslationsCode0
cFineGAN: Unsupervised multi-conditional fine-grained image generation—0
Connecting Vision and Language with Localized NarrativesCode0
MetalGAN: Multi-Domain Label-Less Image Synthesis Using cGANs and Meta-Learning—0
AdversarialNAS: Adversarial Neural Architecture Search for GANsCode0
Hi-CMD: Hierarchical Cross-Modality Disentanglement for Visible-Infrared Person Re-IdentificationCode0
Maximum entropy methods for texture synthesis: theory and practice—0
DEGAS: Differentiable Efficient Generator Search—0
LOGAN: Latent Optimisation for Generative Adversarial NetworksCode0
Implicit Generation and Modeling with Energy Based ModelsCode0
Learn, Imagine and Create: Text-to-Image Generation from Prior KnowledgeCode0
Twin Auxilary Classifiers GANCode0
Quality analysis of DCGAN-generated mammography lesions—0
SEAN: Image Synthesis with Semantic Region-Adaptive NormalizationCode0
Your Local GAN: Designing Two Dimensional Local Attention Mechanisms for Generative ModelsCode0
Example-Guided Scene Image Synthesis using Masked Spatial-Channel Attention and Patch-Based Self-Supervision—0
MixNMatch: Multifactor Disentanglement and Encoding for Conditional Image GenerationCode0
Semantic Bottleneck Scene GenerationCode0
Noise Robust Generative Adversarial NetworksCode0
Progressive Retinex: Mutually Reinforced Illumination-Noise Perception Network for Low Light Image Enhancement—0
Semantic Hierarchy Emerges in Deep Generative Representations for Scene SynthesisCode0
Fine-grained Synthesis of Unrestricted Adversarial Examples—0
Language-based Colorization of Scene SketchesCode0
Self-supervised GAN: Analysis and Improvement with Multi-class Minimax GameCode0
MMGAN: Generative Adversarial Networks for Multi-Modal Distributions—0
Question-Conditioned Counterfactual Image Generation for VQA—0
A Modular Deep Learning Pipeline for Galaxy-Scale Strong Gravitational Lens Detection and Modeling—0
Multimodal Intelligence: Representation Learning, Information Fusion, and Applications—0
Collapse Resistant Deep Convolutional GAN for Multi-Object Image Generation—0
Quality Aware Generative Adversarial NetworksCode0
Pixel-wise Conditioning of Generative Adversarial NetworksCode0
Self-supervised Deformation Modeling for Facial Expression Editing—0
Cali-Sketch: Stroke Calibration and Completion for High-Quality Face Image Generation from Human-Like Sketches—0
Text-to-image synthesis method evaluation based on visual patterns—0
Denoising and Regularization via Exploiting the Structural Bias of Convolutional GeneratorsCode0
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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