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 4601–4625 of 6689 papers

TitleStatusHype
Keys to Better Image Inpainting: Structure and Texture Go Hand in HandCode1
Seamless Iterative Semi-Supervised Correction of Imperfect Labels in Microscopy ImagesCode0
Adversarial Attacks on Image Generation With Made-Up Words—0
Artificial Image Tampering Distorts Spatial Distribution of Texture Landmarks and Quality Characteristics—0
Counterfactual Image Synthesis for Discovery of Personalized Predictive Image Markers—0
Pyramidal Denoising Diffusion Probabilistic Models—0
Character Generation through Self-Supervised Vectorization—0
An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual InversionCode5
Prompt-to-Prompt Image Editing with Cross Attention ControlCode4
Momentum Transformer: Closing the Performance Gap Between Self-attention and Its Linearization—0
Cross Attention Based Style Distribution for Controllable Person Image SynthesisCode1
AdaWCT: Adaptive Whitening and Coloring Style Injection—0
Conservative Generator, Progressive Discriminator: Coordination of Adversaries in Few-shot Incremental Image Synthesis—0
Testing Relational Understanding in Text-Guided Image Generation—0
Generative Steganography Network—0
Automated Classification of Nanoparticles with Various Ultrastructures and Sizes—0
GAUDI: A Neural Architect for Immersive 3D Scene GenerationCode2
PASTA-GAN++: A Versatile Framework for High-Resolution Unpaired Virtual Try-on—0
Generator Knows What Discriminator Should Learn in Unconditional GANsCode1
Vector Quantized Image-to-Image Translation—0
Contrastive Image Synthesis and Self-supervised Feature Adaptation for Cross-Modality Biomedical Image SegmentationCode0
Multi-Forgery Detection Challenge 2022: Push the Frontier of Unconstrained and Diverse Forgery Detection—0
Leveraging GAN Priors for Few-Shot Part SegmentationCode0
Text-Guided Synthesis of Artistic Images with Retrieval-Augmented Diffusion ModelsCode6
KUNet: Imaging Knowledge-Inspired Single HDR Image ReconstructionCode1
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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