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 3051–3075 of 6689 papers

TitleStatusHype
Improved Modeling of 3D Shapes with Multi-view Depth MapsCode0
Improved Conditional Flow Models for Molecule to Image SynthesisCode0
ZstGAN: An Adversarial Approach for Unsupervised Zero-Shot Image-to-Image TranslationCode0
Enhancing Diffusion Models Efficiency by Disentangling Total-Variance and Signal-to-Noise RatioCode0
Improved ArtGAN for Conditional Synthesis of Natural Image and ArtworkCode0
Improving Explicit Spatial Relationships in Text-to-Image Generation through an Automatically Derived DatasetCode0
Enhancing Diffusion-Based Image Synthesis with Robust Classifier GuidanceCode0
Image Synthesis with a Single (Robust) ClassifierCode0
Implicit Dynamical Flow Fusion (IDFF) for Generative ModelingCode0
Enhancing Consistency-Based Image Generation via Adversarialy-Trained Classification and Energy-Based DiscriminationCode0
Implicit Generation and Modeling with Energy Based ModelsCode0
Implicit Generative CopulasCode0
Enhancing Conditional Image Generation with Explainable Latent Space ManipulationCode0
Implementing and Experimenting with Diffusion Models for Text-to-Image GenerationCode0
Implicit competitive regularization in GANsCode0
Cross-Modality Neuroimage Synthesis: A SurveyCode0
Implementing Adaptations for Vision AutoRegressive ModelCode0
Hyperparameter-Free Medical Image Synthesis for Sharing Data and Improving Site-Specific SegmentationCode0
Design a Delicious Lunchbox in StyleCode0
Hyperspectral Blind Unmixing using a Double Deep Image PriorCode0
Implicit Inversion turns CLIP into a DecoderCode0
Imaginative Walks: Generative Random Walk Deviation Loss for Improved Unseen Learning RepresentationCode0
IMAGINE-E: Image Generation Intelligence Evaluation of State-of-the-art Text-to-Image ModelsCode0
Composition and Deformance: Measuring Imageability with a Text-to-Image ModelCode0
Image Translation for Medical Image Generation -- Ischemic Stroke LesionsCode0
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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