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 6151–6175 of 6689 papers

TitleStatusHype
Interactive Image Generation Using Scene Graphs—0
Spatially Constrained GAN for Face and Fashion SynthesisCode0
FCC-GAN: A Fully Connected and Convolutional Net Architecture for GANsCode0
Attention-based Fusion for Multi-source Human Image Generation—0
PasteGAN: A Semi-Parametric Method to Generate Image from Scene GraphCode0
SinGAN: Learning a Generative Model from a Single Natural ImageCode1
3DFaceGAN: Adversarial Nets for 3D Face Representation, Generation, and Translation—0
Learn to synthesize and synthesize to learnCode0
ProbGAN: Towards Probabilistic GAN with Theoretical GuaranteesCode0
Overcoming the Disentanglement vs Reconstruction Trade-off via Jacobian SupervisionCode0
Variational Domain Adaptation—0
VHEGAN: Variational Hetero-Encoder Randomized GAN for Zero-Shot Learning—0
Deli-Fisher GAN: Stable and Efficient Image Generation With Structured Latent Generative Space—0
Probabilistic Semantic Embedding—0
COCO-GAN: Conditional Coordinate Generative Adversarial Network—0
Appearance and Pose-Conditioned Human Image Generation using Deformable GANsCode0
Structured Prediction using cGANs with Fusion Discriminator—0
DiamondGAN: Unified Multi-Modal Generative Adversarial Networks for MRI Sequences SynthesisCode0
TileGAN: Synthesis of Large-Scale Non-Homogeneous TexturesCode0
Deferred Neural Rendering: Image Synthesis using Neural TexturesCode0
High-Resolution Network for Photorealistic Style TransferCode0
Making Convolutional Networks Shift-Invariant AgainCode1
TextKD-GAN: Text Generation using KnowledgeDistillation and Generative Adversarial NetworksCode0
Generating Long Sequences with Sparse TransformersCode3
Deep residual inception encoder–decoder network for medical imaging synthesisCode0
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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