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 51765200 of 6689 papers

TitleStatusHype
Autonomy 2.0: Why is self-driving always 5 years away?0
DynaDog+T: A Parametric Animal Model for Synthetic Canine Image Generation0
Mutually improved endoscopic image synthesis and landmark detection in unpaired image-to-image translationCode0
Combiner: Full Attention Transformer with Sparse Computation CostCode0
Hidden Convexity of Wasserstein GANs: Interpretable Generative Models with Closed-Form SolutionsCode0
Synthesizing Multi-Tracer PET Images for Alzheimer's Disease Patients using a 3D Unified Anatomy-aware Cyclic Adversarial NetworkCode0
Deep Image Synthesis from Intuitive User Input: A Review and Perspectives0
ViTGAN: Training GANs with Vision TransformersCode1
Grid Partitioned Attention: Efficient TransformerApproximation with Inductive Bias for High Resolution Detail GenerationCode0
CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series ImputationCode1
Bi-level Feature Alignment for Versatile Image Translation and ManipulationCode1
FBC-GAN: Diverse and Flexible Image Synthesis via Foreground-Background Composition0
Improving Text-to-Image Synthesis Using Contrastive LearningCode1
DocSynth: A Layout Guided Approach for Controllable Document Image SynthesisCode1
Towards Better Adversarial Synthesis of Human Images from Text0
On The Distribution of Penultimate Activations of Classification Networks0
Explainable Diabetic Retinopathy Detection and Retinal Image GenerationCode1
Variational Diffusion ModelsCode1
A Survey on Adversarial Image Synthesis0
ResViT: Residual vision transformers for multi-modal medical image synthesisCode1
SDL: New data generation tools for full-level annotated document layoutCode0
Are conditional GANs explicitly conditional?0
Visual Conceptual Blending with Large-scale Language and Vision Models0
Dual-Stream Reciprocal Disentanglement Learning for Domain Adaptation Person Re-IdentificationCode0
Diversifying Semantic Image Synthesis and Editing via Class- and Layer-wise VAEsCode0
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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