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 6126–6150 of 6689 papers

TitleStatusHype
Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection—0
KG-GAN: Knowledge-Guided Generative Adversarial Networks—0
Training Generative Adversarial Networks from Incomplete Observations using Factorised DiscriminatorsCode0
JGAN: A Joint Formulation of GAN for Synthesizing Images and Labels—0
Style transfer-based image synthesis as an efficient regularization technique in deep learning—0
Precision-Recall Curves Using Information Divergence Frontiers—0
Synthesizing Images from Spatio-Temporal Representations using Spike-based Backpropagation—0
Generative Latent FlowCode0
Training language GANs from ScratchCode0
Decentralized Learning of Generative Adversarial Networks from Non-iid Data—0
AttentionRNN: A Structured Spatial Attention Mechanism—0
Using Photorealistic Face Synthesis and Domain Adaptation to Improve Facial Expression Analysis—0
Semi-supervised learning based on generative adversarial network: a comparison between good GAN and bad GAN approach—0
Non-Parametric Priors For Generative Adversarial Networks—0
On Conditioning GANs to Hierarchical Ontologies—0
Joint Learning of Neural Networks via Iterative Reweighted Least SquaresCode0
Dilated Spatial Generative Adversarial Networks for Ergodic Image Generation—0
Joint haze image synthesis and dehazing with mmd-vae losses—0
Kernel Mean Matching for Content Addressability of GANsCode0
Hierarchy Composition GAN for High-fidelity Image Synthesis—0
Synthetic-Neuroscore: Using A Neuro-AI Interface for Evaluating Generative Adversarial NetworksCode1
Ink removal from histopathology whole slide images by combining classification, detection and image generation modelsCode0
Which Contrast Does Matter? Towards a Deep Understanding of MR Contrast using Collaborative GANCode0
The Art of Food: Meal Image Synthesis from IngredientsCode0
Grand Challenge of 106-Point Facial Landmark Localization—0
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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