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 5951–5975 of 6689 papers

TitleStatusHype
Progressive Retinex: Mutually Reinforced Illumination-Noise Perception Network for Low Light Image Enhancement—0
MixNMatch: Multifactor Disentanglement and Encoding for Conditional Image GenerationCode0
Semantic Hierarchy Emerges in Deep Generative Representations for Scene SynthesisCode0
Fine-grained Synthesis of Unrestricted Adversarial Examples—0
Language-based Colorization of Scene SketchesCode0
Self-supervised GAN: Analysis and Improvement with Multi-class Minimax GameCode0
MMGAN: Generative Adversarial Networks for Multi-Modal Distributions—0
Question-Conditioned Counterfactual Image Generation for VQA—0
A Modular Deep Learning Pipeline for Galaxy-Scale Strong Gravitational Lens Detection and Modeling—0
Multimodal Intelligence: Representation Learning, Information Fusion, and Applications—0
Collapse Resistant Deep Convolutional GAN for Multi-Object Image Generation—0
Quality Aware Generative Adversarial NetworksCode0
Multi-marginal Wasserstein GANCode1
Self-supervised Deformation Modeling for Facial Expression Editing—0
Pixel-wise Conditioning of Generative Adversarial NetworksCode0
Cali-Sketch: Stroke Calibration and Completion for High-Quality Face Image Generation from Human-Like Sketches—0
Text-to-image synthesis method evaluation based on visual patterns—0
Denoising and Regularization via Exploiting the Structural Bias of Convolutional GeneratorsCode0
Small-GAN: Speeding Up GAN Training Using Core-sets—0
Semantic Object Accuracy for Generative Text-to-Image SynthesisCode0
Consistency Regularization for Generative Adversarial Networks—0
Fair Generative Modeling via Weak SupervisionCode0
Study of Deep Generative Models for Inorganic Chemical CompositionsCode0
TRB: A Novel Triplet Representation for Understanding 2D Human BodyCode0
Unsupervised Robust Disentangling of Latent Characteristics for Image Synthesis—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