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 18761900 of 6689 papers

TitleStatusHype
Continuous Conditional Generative Adversarial Networks: Novel Empirical Losses and Label Input MechanismsCode1
Text-to-Image Generation Grounded by Fine-Grained User AttentionCode1
Towards Disentangling Latent Space for Unsupervised Semantic Face EditingCode1
CooGAN: A Memory-Efficient Framework for High-Resolution Facial Attribute EditingCode1
Enhanced Balancing GAN: Minority-class Image GenerationCode1
MichiGAN: Multi-Input-Conditioned Hair Image Generation for Portrait EditingCode1
Generative Neurosymbolic MachinesCode1
Few-Shot Adaptation of Generative Adversarial NetworksCode1
One Model to Reconstruct Them All: A Novel Way to Use the Stochastic Noise in StyleGANCode1
Synthesis of COVID-19 Chest X-rays using Unpaired Image-to-Image TranslationCode1
Unsupervised Image-to-Image Translation via Pre-trained StyleGAN2 NetworkCode1
Resolution Dependent GAN Interpolation for Controllable Image Synthesis Between DomainsCode1
Image Generation With Neural Cellular AutomatasCode1
Deep-Masking Generative Network: A Unified Framework for Background Restoration from Superimposed ImagesCode1
Evaluating the Clinical Realism of Synthetic Chest X-Rays Generated Using Progressively Growing GANsCode1
A disentangled generative model for disease decomposition in chest X-rays via normal image synthesisCode1
Data Augmentation Based Malware Detection using Convolutional Neural NetworksCode1
Improving Relational Regularized Autoencoders with Spherical Sliced Fused Gromov WassersteinCode1
Implicit Rank-Minimizing AutoencoderCode1
VAEBM: A Symbiosis between Variational Autoencoders and Energy-based ModelsCode1
RG-Flow: A hierarchical and explainable flow model based on renormalization group and sparse priorCode1
TinyGAN: Distilling BigGAN for Conditional Image GenerationCode1
X-LXMERT: Paint, Caption and Answer Questions with Multi-Modal TransformersCode1
DeltaGAN: Towards Diverse Few-shot Image Generation with Sample-Specific DeltaCode1
Multi-Spectral Image Synthesis for Crop/Weed Segmentation in Precision FarmingCode1
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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