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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5051–5100 of 6689 papers

TitleStatusHype
Distinguishing Natural and Computer-Generated Images using Multi-Colorspace fused EfficientNetCode0
Illiterate DALL-E Learns to ComposeCode1
MaGNET: Uniform Sampling from Deep Generative Network Manifolds Without RetrainingCode1
Multi-Tailed, Multi-Headed, Spatial Dynamic Memory refined Text-to-Image Synthesis—0
Pose-guided Generative Adversarial Net for Novel View Action Synthesis—0
Diffusion Normalizing FlowCode1
THOMAS: Trajectory Heatmap Output with learned Multi-Agent Sampling—0
The Deep Generative Decoder: MAP estimation of representations improves modeling of single-cell RNA dataCode1
DeepVecFont: Synthesizing High-quality Vector Fonts via Dual-modality LearningCode1
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
Exploring constraints on CycleGAN-based CBCT enhancement for adaptive radiotherapyCode1
Vector-quantized Image Modeling with Improved VQGANCode1
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
Generative Modeling with Optimal Transport MapsCode1
ClimateGAN: Raising Climate Change Awareness by Generating Images of FloodsCode1
Meta Internal LearningCode1
DiffusionCLIP: Text-Guided Diffusion Models for Robust Image ManipulationCode1
CLIP-Forge: Towards Zero-Shot Text-to-Shape GenerationCode1
FooDI-ML: a large multi-language dataset of food, drinks and groceries images and descriptionsCode1
Autoregressive Diffusion ModelsCode1
GenCo: Generative Co-training for Generative Adversarial Networks with Limited DataCode1
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
UNCERTAINTY QUANTIFICATION USING VARIATIONAL INFERENCE FOR BIOMEDICAL IMAGE SEGMENTATION—0
A Scaling Law for Syn-to-Real Transfer: How Much Is Your Pre-training Effective?—0
Improved Image Generation via Sparsity—0
A Flexible Measurement of Diversity in Datasets with Random Network Distillation—0
An Improved Composite Functional Gradient Learning by Wasserstein Regularization for Generative adversarial networks—0
ST-DDPM: Explore Class Clustering for Conditional Diffusion Probabilistic Models—0
Distilling GANs with Style-Mixed Triplets for X2I Translation with Limited Data—0
High Precision Score-based Diffusion Models—0
Maximum Likelihood Training of Parametrized Diffusion Model—0
Towards Generative Latent Variable Models for Speech—0
Optimizing Few-Step Diffusion Samplers by Gradient Descent—0
Exploiting Knowledge Distillation for Few-Shot Image Generation—0
Evolutionary perspective on model fine-tuning—0
Illiterate DALLE Learns to Compose—0
Causal-TGAN: Causally-Aware Synthetic Tabular Data Generative Adversarial Network—0
FastEnsemble: Benchmarking and Accelerating Ensemble-based Uncertainty Estimation for Image-to-Image Translation—0
HyperCGAN: Text-to-Image Synthesis with HyperNet-Modulated Conditional Generative Adversarial Networks—0
Prototype memory and attention mechanisms for few shot image generation—0
DRAN: Detailed Region-Adaptive Normalization for Conditional Image SynthesisCode0
Neural Knitworks: Patched Neural Implicit Representation Networks—0
USIS: Unsupervised Semantic Image SynthesisCode0
Designing Counterfactual Generators using Deep Model Inversion—0
Implicit Generative CopulasCode0
Show:102550
← PrevPage 102 of 134Next →
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