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 5251–5300 of 6689 papers

TitleStatusHype
Effect of Instance Normalization on Fine-Grained Control for Sketch-Based Face Image Generation—0
Accurate Ground-Truth Depth Image Generation via Overfit Training of Point Cloud Registration using Local Frame Sets—0
Face editing with GAN -- A Review—0
Going the Extra Mile in Face Image Quality Assessment: A Novel Database and Model—0
Generative Adversarial Networks and Other Generative Models—0
Exploring Generative Adversarial Networks for Text-to-Image Generation with Evolution StrategiesCode0
Text to Image Synthesis using Stacked Conditional Variational Autoencoders and Conditional Generative Adversarial Networks—0
Histopathology DatasetGAN: Synthesizing Large-Resolution Histopathology DatasetsCode0
Array Camera Image Fusion using Physics-Aware TransformersCode0
Backdoor Attack is a Devil in Federated GAN-based Medical Image SynthesisCode0
Transforming Image Generation from Scene Graphs—0
AS-IntroVAE: Adversarial Similarity Distance Makes Robust IntroVAECode0
Your Autoregressive Generative Model Can be Better If You Treat It as an Energy-Based One—0
Megapixel Image Generation with Step-Unrolled Denoising AutoencodersCode0
Mitigating Presentation Attack using DCGAN and Deep CNN—0
Scaling Autoregressive Models for Content-Rich Text-to-Image GenerationCode0
Temporally Consistent Semantic Video Editing—0
CS^2: A Controllable and Simultaneous Synthesizer of Images and Annotations with Minimal Human InterventionCode0
DALL-E for Detection: Language-driven Compositional Image Synthesis for Object Detection—0
ConvGeN: Convex space learning improves deep-generative oversampling for tabular imbalanced classification on smaller datasetsCode0
0/1 Deep Neural Networks via Block Coordinate Descent—0
Minimum Noticeable Difference based Adversarial Privacy Preserving Image Generation—0
Unified-IO: A Unified Model for Vision, Language, and Multi-Modal Tasks—0
Controllable 3D Face Synthesis with Conditional Generative Occupancy Fields—0
GRAM-HD: 3D-Consistent Image Generation at High Resolution with Generative Radiance Manifolds—0
Image Generation with Multimodal Priors using Denoising Diffusion Probabilistic Models—0
Dilated POCS: Minimax Convex Optimization—0
Draft-and-Revise: Effective Image Generation with Contextual RQ-Transformer—0
Accelerating Score-based Generative Models for High-Resolution Image Synthesis—0
Scene Aware Person Image Generation through Global Contextual Conditioning—0
Day-to-Night Image Synthesis for Training Nighttime Neural ISPsCode0
AUTM Flow: Atomic Unrestricted Time Machine for Monotonic Normalizing Flows—0
Computer Vision-based Characterization of Large-scale Jet Flames using a Synthetic Infrared Image Generation Approach—0
Style-Content Disentanglement in Language-Image Pretraining Representations for Zero-Shot Sketch-to-Image Synthesis—0
Towards Improving the Generation Quality of Autoregressive Slot VAEsCode0
Modular StoryGAN with Background and Theme Awareness for Story VisualizationCode0
DiVAE: Photorealistic Images Synthesis with Denoising Diffusion Decoder—0
PAGER: Progressive Attribute-Guided Extendable Robust Image GenerationCode0
From Keypoints to Object Landmarks via Self-Training Correspondence: A novel approach to Unsupervised Landmark DiscoveryCode0
MontageGAN: Generation and Assembly of Multiple Components by GANsCode0
Learning with Stochastic OrdersCode0
Analytical Interpretation of Latent Codes in InfoGAN with SAR Images—0
Analyzing the Latent Space of GAN through Local Dimension Estimation—0
Pre-trained Perceptual Features Improve Differentially Private Image GenerationCode0
Text-to-Face Generation with StyleGAN2—0
Structured Uncertainty in the Observation Space of Variational AutoencodersCode0
M6-Fashion: High-Fidelity Multi-modal Image Generation and Editing—0
Improving Human Image Synthesis with Residual Fast Fourier Transformation and Wasserstein Distance—0
EBM Life Cycle: MCMC Strategies for Synthesis, Defense, and Density ModelingCode0
GR-GAN: Gradual Refinement Text-to-image GenerationCode0
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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