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 6401–6425 of 6689 papers

TitleStatusHype
Efficient Subsampling of Realistic Images From GANs Conditional on a Class or a Continuous VariableCode0
Autoregressive Omni-Aware Outpainting for Open-Vocabulary 360-Degree Image GenerationCode0
Learning to Predict Layout-to-image Conditional Convolutions for Semantic Image SynthesisCode0
Going beyond Compositions, DDPMs Can Produce Zero-Shot InterpolationsCode0
Learning to Rearrange Voxels in Binary Segmentation Masks for Smooth Manifold TriangulationCode0
Dist-GAN: An Improved GAN using Distance ConstraintsCode0
Quality Aware Generative Adversarial NetworksCode0
SEAN: Image Synthesis with Semantic Region-Adaptive NormalizationCode0
Quality Guided Sketch-to-Photo Image SynthesisCode0
Auto-painter: Cartoon Image Generation from Sketch by Using Conditional Generative Adversarial NetworksCode0
GP-GAN: Towards Realistic High-Resolution Image BlendingCode0
Quantifying the effect of X-ray scattering for data generation in real-time defect detectionCode0
Searching towards Class-Aware Generators for Conditional Generative Adversarial NetworksCode0
Object-driven Text-to-Image Synthesis via Adversarial TrainingCode0
Class-Continuous Conditional Generative Neural Radiance FieldCode0
Class-Distinct and Class-Mutual Image Generation with GANsCode0
Observation-Guided Diffusion Probabilistic ModelsCode0
Automatic Semantic Style Transfer using Deep Convolutional Neural Networks and Soft MasksCode0
GradBias: Unveiling Word Influence on Bias in Text-to-Image Generative ModelsCode0
Gradient Adjusting Networks for Domain InversionCode0
Learning with Stochastic OrdersCode0
Gradient Estimators for Implicit ModelsCode0
Learn to synthesize and synthesize to learnCode0
Straight-Line Diffusion Model for Efficient 3D Molecular GenerationCode0
Quantum Diffusion Model for Quark and Gluon Jet GenerationCode0
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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