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 5501–5550 of 6689 papers

TitleStatusHype
Multi-Tailed, Multi-Headed, Spatial Dynamic Memory refined Text-to-Image Synthesis—0
Pose-guided Generative Adversarial Net for Novel View Action Synthesis—0
THOMAS: Trajectory Heatmap Output with learned Multi-Agent Sampling—0
Deep Human-guided Conditional Variational Generative Modeling for Automated Urban Planning—0
Discovery of Single Independent Latent VariableCode0
Unsupervised High-Fidelity Facial Texture Generation and Reconstruction—0
Collaging Class-specific GANs for Semantic Image Synthesis—0
Flow Plugin Network for conditional generationCode0
Inter-Domain Alignment for Predicting High-Resolution Brain Networks Using Teacher-Student LearningCode0
Learning of Inter-Label Geometric Relationships Using Self-Supervised Learning: Application To Gleason Grade Segmentation—0
AffectGAN: Affect-Based Generative Art Driven by Semantics—0
Improved Image Generation via Sparsity—0
Towards Generative Latent Variable Models for Speech—0
An Improved Composite Functional Gradient Learning by Wasserstein Regularization for Generative adversarial networks—0
FastEnsemble: Benchmarking and Accelerating Ensemble-based Uncertainty Estimation for Image-to-Image Translation—0
Designing Counterfactual Generators using Deep Model Inversion—0
Illiterate DALLE Learns to Compose—0
HyperCGAN: Text-to-Image Synthesis with HyperNet-Modulated Conditional Generative Adversarial Networks—0
Maximum Likelihood Training of Parametrized Diffusion Model—0
DRAN: Detailed Region-Adaptive Normalization for Conditional Image SynthesisCode0
Generative Probabilistic Image Colorization—0
USIS: Unsupervised Semantic Image SynthesisCode0
Implicit Generative CopulasCode0
Distilling GANs with Style-Mixed Triplets for X2I Translation with Limited Data—0
Optimizing Few-Step Diffusion Samplers by Gradient Descent—0
A Flexible Measurement of Diversity in Datasets with Random Network Distillation—0
Exploiting Knowledge Distillation for Few-Shot Image Generation—0
Causal-TGAN: Causally-Aware Synthetic Tabular Data Generative Adversarial Network—0
High Precision Score-based Diffusion Models—0
UNCERTAINTY QUANTIFICATION USING VARIATIONAL INFERENCE FOR BIOMEDICAL IMAGE SEGMENTATION—0
Evolutionary perspective on model fine-tuning—0
Neural Knitworks: Patched Neural Implicit Representation Networks—0
ST-DDPM: Explore Class Clustering for Conditional Diffusion Probabilistic Models—0
Prototype memory and attention mechanisms for few shot image generation—0
A Scaling Law for Syn-to-Real Transfer: How Much Is Your Pre-training Effective?—0
Weakly Supervised Keypoint Discovery—0
NeurInt-Learning Interpolation by Neural ODEs—0
Fine-Grained Image Generation from Bangla Text Description using Attentional Generative Adversarial NetworkCode0
LDC-VAE: A Latent Distribution Consistency Approach to Variational AutoEncoders—0
ComicGAN: Text-to-Comic Generative Adversarial Network—0
Random Multi-Channel Image Synthesis for Multiplexed Immunofluorescence Imaging—0
SketchHairSalon: Deep Sketch-based Hair Image Synthesis—0
Image Synthesis via Semantic Composition—0
The State of the Art when using GPUs in Devising Image Generation Methods Using Deep Learning—0
Pose with Style: Detail-Preserving Pose-Guided Image Synthesis with Conditional StyleGAN—0
Conditional MoCoGAN for Zero-Shot Video Generation—0
On the Sins of Image Synthesis Loss for Self-supervised Depth Estimation—0
MSGDD-cGAN: Multi-Scale Gradients Dual Discriminator Conditional Generative Adversarial Network—0
Per Garment Capture and Synthesis for Real-time Virtual Try-on—0
Rethinking Multidimensional Discriminator Output for Generative Adversarial Networks—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