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 4751–4800 of 6689 papers

TitleStatusHype
Resnet18 Model With Sequential Layer For Computing Accuracy On Image Classification Dataset—0
Language-agnostic Semantic Consistent Text-to-Image Generation—0
OSSGAN: Open-Set Semi-Supervised Image GenerationCode1
CogView2: Faster and Better Text-to-Image Generation via Hierarchical TransformersCode2
Oracle Guided Image Synthesis with Relative QueriesCode1
One Model to Synthesize Them All: Multi-contrast Multi-scale Transformer for Missing Data Imputation—0
Optimized latent-code selection for explainable conditional text-to-image GANs—0
Self-Supervised Text Erasing with Controllable Image Synthesis—0
Assessing the ability of generative adversarial networks to learn canonical medical image statistics—0
Semi-Parametric Neural Image SynthesisCode6
StyleGAN-Human: A Data-Centric Odyssey of Human GenerationCode3
BCI: Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pixCode2
Transfer Learning from Synthetic In-vitro Soybean Pods Dataset for In-situ Segmentation of On-branch Soybean Pod—0
Reinforcing Generated Images via Meta-learning for One-Shot Fine-Grained Visual Recognition—0
A Taxonomy of Prompt Modifiers for Text-To-Image Generation—0
DAM-GAN : Image Inpainting using Dynamic Attention Map based on Fake Texture Detection—0
Dual-Domain Image Synthesis using Segmentation-Guided GANCode1
Simultaneous Multiple-Prompt Guided Generation Using Differentiable Optimal Transport—0
A Comprehensive Survey on Data-Efficient GANs in Image Generation—0
VQGAN-CLIP: Open Domain Image Generation and Editing with Natural Language GuidanceCode2
DR-GAN: Distribution Regularization for Text-to-Image GenerationCode0
StyleT2F: Generating Human Faces from Textual Description Using StyleGAN2Code0
Guided Co-Modulated GAN for 360° Field of View Extrapolation—0
Sketch guided and progressive growing GAN for realistic and editable ultrasound image synthesis—0
Any-resolution Training for High-resolution Image SynthesisCode2
Migrating Face Swap to Mobile Devices: A lightweight Framework and A Supervised Training SolutionCode1
Hierarchical Text-Conditional Image Generation with CLIP LatentsCode3
Neural Texture Extraction and Distribution for Controllable Person Image SynthesisCode2
Multi-View Consistent Generative Adversarial Networks for 3D-aware Image SynthesisCode1
No Token Left Behind: Explainability-Aided Image Classification and GenerationCode1
ManiTrans: Entity-Level Text-Guided Image Manipulation via Token-wise Semantic Alignment and GenerationCode0
Noise-based Enhancement for Foveated Rendering—0
Evaluating Procedures for Establishing Generative Adversarial Network-based Stochastic Image Models in Medical Imaging—0
Practical Digital Disguises: Leveraging Face Swaps to Protect Patient Privacy—0
Thermal to Visible Image Synthesis under Atmospheric Turbulence—0
The Swiss Army Knife for Image-to-Image Translation: Multi-Task Diffusion Models—0
Learning to Generate Realistic Noisy Images via Pixel-level Noise-aware Adversarial TrainingCode1
Retrieval-based Spatially Adaptive Normalization for Semantic Image SynthesisCode1
KNN-Diffusion: Image Generation via Large-Scale Retrieval—0
Arbitrary-Scale Image SynthesisCode1
FaceSigns: Semi-Fragile Neural Watermarks for Media Authentication and Countering DeepfakesCode1
DT2I: Dense Text-to-Image Generation from Region Descriptions—0
Flexible Portrait Image Editing with Fine-Grained Control—0
3D microstructural generation from 2D images of cement paste using generative adversarial networksCode0
Estimating Fine-Grained Noise Model via Contrastive Learning—0
Adversarially robust segmentation models learn perceptually-aligned gradients—0
PixelFolder: An Efficient Progressive Pixel Synthesis Network for Image GenerationCode1
IR-GAN: Image Manipulation with Linguistic Instruction by Increment Reasoning—0
Fast and Automatic Object Registration for Human-Robot Collaboration in Industrial Manufacturing—0
Marginal Contrastive Correspondence for Guided Image Generation—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