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 2051–2075 of 6689 papers

TitleStatusHype
Residual Flows for Invertible Generative ModelingCode1
Generative Adversarial Networks in Computer Vision: A Survey and TaxonomyCode1
Generating Diverse High-Fidelity Images with VQ-VAE-2Code1
Synthetic-Neuroscore: Using A Neuro-AI Interface for Evaluating Generative Adversarial NetworksCode1
SinGAN: Learning a Generative Model from a Single Natural ImageCode1
Making Convolutional Networks Shift-Invariant AgainCode1
Improved Precision and Recall Metric for Assessing Generative ModelsCode1
Sliced Wasserstein Generative ModelsCode1
Progressive Pose Attention Transfer for Person Image GenerationCode1
Image Generation From Small Datasets via Batch Statistics AdaptationCode1
DM-GAN: Dynamic Memory Generative Adversarial Networks for Text-to-Image SynthesisCode1
Disentangled Representation Learning in Cardiac Image AnalysisCode1
Semantic Image Synthesis with Spatially-Adaptive NormalizationCode1
Unsupervised Part-Based Disentangling of Object Shape and AppearanceCode1
MaCow: Masked Convolutional Generative FlowCode1
BIVA: A Very Deep Hierarchy of Latent Variables for Generative ModelingCode1
FaceForensics++: Learning to Detect Manipulated Facial ImagesCode1
Diverse Image Synthesis from Semantic Layouts via Conditional IMLECode1
Invertible Residual NetworksCode1
Towards High Resolution Video Generation with Progressive Growing of Sliced Wasserstein GANsCode1
FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative ModelsCode1
Large Scale GAN Training for High Fidelity Natural Image SynthesisCode1
Cross-view image synthesis using geometry-guided conditional GANsCode1
Glow: Generative Flow with Invertible 1x1 ConvolutionsCode1
Fashion-Gen: The Generative Fashion Dataset and ChallengeCode1
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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