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

Showing 63016325 of 6689 papers

TitleStatusHype
NiNformer: A Network in Network Transformer with Token Mixing Generated Gating FunctionCode0
Data-driven Crop Growth Simulation on Time-varying Generated Images using Multi-conditional Generative Adversarial NetworksCode0
Generative Latent FlowCode0
Generative Max-Mahalanobis Classifiers for Image Classification, Generation and MoreCode0
Generative Model-Driven Synthetic Training Image Generation: An Approach to Cognition in Rail Defect DetectionCode0
Category-aware EEG image generation based on wavelet transform and contrast semantic lossCode0
Spectrum Translation for Refinement of Image Generation (STIG) Based on Contrastive Learning and Spectral Filter ProfileCode0
A Somewhat Robust Image Watermark against Diffusion-based Editing ModelsCode0
Generative Modeling of Microweather Wind Velocities for Urban Air MobilityCode0
Generative modeling of seismic data using score-based generative modelsCode0
DISGAN: Wavelet-informed Discriminator Guides GAN to MRI Super-resolution with Noise CleaningCode0
Generative Modeling with Explicit MemoryCode0
Prompt to Polyp: Medical Text-Conditioned Image Synthesis with Diffusion ModelsCode0
TF-Replicator: Distributed Machine Learning for ResearchersCode0
Dynamic Importance in Diffusion U-Net for Enhanced Image SynthesisCode0
UWAFA-GAN: Ultra-Wide-Angle Fluorescein Angiography Transformation via Multi-scale Generation and Registration EnhancementCode0
Generative modelling with jump-diffusionsCode0
CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion ModelsCode0
Generative Models from the perspective of Continual LearningCode0
Generative Multi-Adversarial NetworksCode0
Learning Disentangled Discrete RepresentationsCode0
TGAN: Deep Tensor Generative Adversarial Nets for Large Image GenerationCode0
Data-Efficient Molecular Generation with Hierarchical Textual InversionCode0
Noise Diffusion for Enhancing Semantic Faithfulness in Text-to-Image SynthesisCode0
Partial Label Supervision for Agnostic Generative Noisy Label LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Improved DDPMFID12.3Unverified
2ADMFID11.84Unverified
3BigGAN-deepFID8.1Unverified
4Polarity-BigGANFID6.82Unverified
5VQGAN+Transformer (k=mixed, p=1.0, a=0.005)FID6.59Unverified
6MaskGITFID6.18Unverified
7VQGAN+Transformer (k=600, p=1.0, a=0.05)FID5.2Unverified
8CDMFID4.88Unverified
9ADM-GFID4.59Unverified
10RINFID4.51Unverified
#ModelMetricClaimedVerifiedStatus
1PresGANFID52.2Unverified
2RESFLOWFID48.29Unverified
3Residual FlowFID46.37Unverified
4GLF+perceptual loss (ours)FID44.6Unverified
5ProdPoly no activation functionsFID40.45Unverified
6ProdPoly no activation functionsFID36.77Unverified
7ACGANFID35.47Unverified
8DenseFlow-74-10FID34.9Unverified
9NVAE w/ flowFID32.53Unverified
10QSNGANFID31.97Unverified
#ModelMetricClaimedVerifiedStatus
1GLIDE + CLSFID30.87Unverified
2GLIDE + CLIPFID30.46Unverified
3GLIDE + CLS-FREEFID29.22Unverified
4GLIDE + CLIP + CLS + CLS-FREEFID29.18Unverified
5PGMGANFID21.73Unverified
6CLR-GANFID20.27Unverified
7FMFID14.45Unverified
8CT (Direct Generation, NFE=1)FID13Unverified
9CT (Direct Generation, NFE=2)FID11.1Unverified
10GLIDE +CLSKID7.95Unverified