task_path stringlengths 3 199 ⌀ | dataset stringlengths 1 128 ⌀ | model_name stringlengths 1 223 ⌀ | paper_url stringlengths 21 601 ⌀ | metric_name stringlengths 1 50 ⌀ | metric_value stringlengths 1 9.22k ⌀ |
|---|---|---|---|---|---|
Medical Image Segmentation > Lung Nodule Segmentation | LUNA | BCDU-Net (d=3) | https://arxiv.org/abs/1909.00166v1 | F1 score | 0.9904 |
Medical Image Segmentation > Lung Nodule Segmentation | LUNA | BCDU-Net (d=3) | https://arxiv.org/abs/1909.00166v1 | AUC | 0.9946 |
Medical Image Segmentation > Lung Nodule Segmentation | LUNA | Residual U-Net | http://arxiv.org/abs/1711.10684v1 | F1 score | 0.9690 |
Medical Image Segmentation > Lung Nodule Segmentation | LUNA | Residual U-Net | http://arxiv.org/abs/1711.10684v1 | AUC | 0.9849 |
Medical Image Segmentation > Lung Nodule Segmentation | LUNA | U-Net | http://arxiv.org/abs/1505.04597v1 | F1 score | 0.9658 |
Medical Image Segmentation > Lung Nodule Segmentation | LUNA | U-Net | http://arxiv.org/abs/1505.04597v1 | AUC | 0.9784 |
Medical Image Segmentation > Lung Nodule Segmentation | LUNA | CE-Net | http://arxiv.org/abs/1903.02740v1 | Accuracy | 0.99 |
Medical Image Segmentation > Lung Nodule Segmentation | LUNA | ET-Net | https://arxiv.org/abs/1907.10936v1 | Accuracy | 0.9868 |
Medical Image Segmentation > Lung Nodule Segmentation | LUNA | ET-Net | https://arxiv.org/abs/1907.10936v1 | mIoU | 0.9623 |
Medical Image Segmentation > Lung Nodule Segmentation | NIH | U-Net+R+A4 | http://arxiv.org/abs/1904.09229v1 | AVD | 0.262 |
Medical Image Segmentation > Lung Nodule Segmentation | NIH | U-Net+R+A4 | http://arxiv.org/abs/1904.09229v1 | Dice Score | 0.962 |
Medical Image Segmentation > Lung Nodule Segmentation | NIH | U-Net+R+A4 | http://arxiv.org/abs/1904.09229v1 | Precision | 0.969 |
Medical Image Segmentation > Lung Nodule Segmentation | NIH | U-Net+R+A4 | http://arxiv.org/abs/1904.09229v1 | Recall | 0.956 |
Medical Image Segmentation > Lung Nodule Segmentation | NIH | U-Net+R+A4 | http://arxiv.org/abs/1904.09229v1 | VS | 0.985 |
Medical Image Segmentation > Iris Segmentation | CASIA | IrisParseNet (ASPP) CASIA | https://arxiv.org/abs/1901.11195v2 | F1 | 94.30 |
Medical Image Segmentation > Iris Segmentation | CASIA | IrisParseNet (ASPP) CASIA | https://arxiv.org/abs/1901.11195v2 | mIoU | 89.4 |
Medical Image Segmentation > Iris Segmentation | MICHE | IrisParseNet (PSP) | https://arxiv.org/abs/1901.11195v2 | F1 | 91.5 |
Medical Image Segmentation > Iris Segmentation | MICHE | IrisParseNet (PSP) | https://arxiv.org/abs/1901.11195v2 | mIoU | 85.07 |
Medical Image Segmentation > Iris Segmentation | UBIRIS | IrisParseNet (ASPP) | https://arxiv.org/abs/1901.11195v2 | F1 | 91.82 |
Medical Image Segmentation > Iris Segmentation | UBIRIS | IrisParseNet (ASPP) | https://arxiv.org/abs/1901.11195v2 | mIoU | 85.39 |
Medical Image Segmentation > Nuclear Segmentation | Cell17 | Cell R-CNN | https://doi.org/10.1007/978-3-030-00934-2_27 | F1-score | 0.8216 |
Medical Image Segmentation > Nuclear Segmentation | Cell17 | Cell R-CNN | https://doi.org/10.1007/978-3-030-00934-2_27 | Dice | 0.7088 |
Medical Image Segmentation > Nuclear Segmentation | Cell17 | Cell R-CNN | https://doi.org/10.1007/978-3-030-00934-2_27 | Hausdorff | 11.3141 |
Medical Image Segmentation > Nuclear Segmentation | Cell17 | Mask R-CNN | http://arxiv.org/abs/1703.06870v3 | F1-score | 0.8004 |
Medical Image Segmentation > Nuclear Segmentation | Cell17 | Mask R-CNN | http://arxiv.org/abs/1703.06870v3 | Dice | 0.707 |
Medical Image Segmentation > Nuclear Segmentation | Cell17 | Mask R-CNN | http://arxiv.org/abs/1703.06870v3 | Hausdorff | 12.6723 |
Medical Image Segmentation > Nuclear Segmentation | Cell17 | Pix2Pix | http://arxiv.org/abs/1611.07004v3 | F1-score | 0.6208 |
Medical Image Segmentation > Nuclear Segmentation | Cell17 | Pix2Pix | http://arxiv.org/abs/1611.07004v3 | Dice | 0.6351 |
Medical Image Segmentation > Nuclear Segmentation | Cell17 | Pix2Pix | http://arxiv.org/abs/1611.07004v3 | Hausdorff | 19.1441 |
Medical Image Segmentation > Nuclear Segmentation | Cell17 | FnsNet | http://arxiv.org/abs/1603.08155v1 | F1-score | 0.7413 |
Medical Image Segmentation > Nuclear Segmentation | Cell17 | FnsNet | http://arxiv.org/abs/1603.08155v1 | Dice | 0.6165 |
Medical Image Segmentation > Nuclear Segmentation | Cell17 | FnsNet | http://arxiv.org/abs/1603.08155v1 | Hausdorff | 25.9102 |
Medical Image Segmentation > Skin Cancer Segmentation | Kaggle Skin Lesion Segmentation | R2U-Net | http://arxiv.org/abs/1802.06955v5 | F1 score | 0.8920 |
Medical Image Segmentation > Skin Cancer Segmentation | Kaggle Skin Lesion Segmentation | R2U-Net | http://arxiv.org/abs/1802.06955v5 | AUC | 0.9419 |
Medical Image Segmentation > Skin Cancer Segmentation | Kaggle Skin Lesion Segmentation | Residual U-Net | http://arxiv.org/abs/1711.10684v1 | F1 score | 0.8799 |
Medical Image Segmentation > Skin Cancer Segmentation | Kaggle Skin Lesion Segmentation | Residual U-Net | http://arxiv.org/abs/1711.10684v1 | AUC | 0.9396 |
Medical Image Segmentation > Skin Cancer Segmentation | Kaggle Skin Lesion Segmentation | U-Net | http://arxiv.org/abs/1505.04597v1 | F1 score | 0.8682 |
Medical Image Segmentation > Skin Cancer Segmentation | Kaggle Skin Lesion Segmentation | U-Net | http://arxiv.org/abs/1505.04597v1 | AUC | 0.9371 |
Medical Image Segmentation > Skin Cancer Segmentation | PH2 | SegNet | https://raw.githubusercontent.com/hashbanger/Skin_Lesion_Segmentation/master/abstract.txt | IoU | 93.61 |
Medical Image Segmentation > Electron Microscopy Image Segmentation | SNEMI3D | DTN | https://doi.org/10.24963/ijcai.2019/401 | AUC | 0.8953 |
Medical Image Segmentation > Electron Microscopy Image Segmentation | SNEMI3D | U-Net | http://arxiv.org/abs/1505.04597v1 | AUC | 0.8676 |
Medical Image Segmentation > Electron Microscopy Image Segmentation | SNEMI3D | Waterz (3D U-Net) + Refinement | http://openaccess.thecvf.com/content_CVPR_2019/html/Matejek_Biologically-Constrained_Graphs_for_Global_Connectomics_Reconstruction_CVPR_2019_paper.html | Total Variation of Information | 0.647 |
Medical Image Segmentation > Electron Microscopy Image Segmentation | SNEMI3D | Waterz (3D U-Net) + Refinement | http://openaccess.thecvf.com/content_CVPR_2019/html/Matejek_Biologically-Constrained_Graphs_for_Global_Connectomics_Reconstruction_CVPR_2019_paper.html | VI Split | 0.438 |
Medical Image Segmentation > Electron Microscopy Image Segmentation | SNEMI3D | Waterz (3D U-Net) + Refinement | http://openaccess.thecvf.com/content_CVPR_2019/html/Matejek_Biologically-Constrained_Graphs_for_Global_Connectomics_Reconstruction_CVPR_2019_paper.html | VI Merge | 0.209 |
Medical Image Segmentation > Electron Microscopy Image Segmentation | SNEMI3D | Waterz (3D U-Net) | http://openaccess.thecvf.com/content_CVPR_2019/html/Matejek_Biologically-Constrained_Graphs_for_Global_Connectomics_Reconstruction_CVPR_2019_paper.html | Total Variation of Information | 0.807 |
Medical Image Segmentation > Electron Microscopy Image Segmentation | SNEMI3D | Waterz (3D U-Net) | http://openaccess.thecvf.com/content_CVPR_2019/html/Matejek_Biologically-Constrained_Graphs_for_Global_Connectomics_Reconstruction_CVPR_2019_paper.html | VI Split | 0.571 |
Medical Image Segmentation > Electron Microscopy Image Segmentation | SNEMI3D | Waterz (3D U-Net) | http://openaccess.thecvf.com/content_CVPR_2019/html/Matejek_Biologically-Constrained_Graphs_for_Global_Connectomics_Reconstruction_CVPR_2019_paper.html | VI Merge | 0.236 |
Medical Image Segmentation > Electron Microscopy Image Segmentation | EMOrganelles | CEM500K-moco | https://www.biorxiv.org/content/10.1101/2020.12.11.421792v1 | Average IOU | 0.7 |
Medical Image Segmentation > Electron Microscopy Image Segmentation | 3D Platelet EM | Hybrid 2D-3D Segmentation Net | https://rdcu.be/cfa43 | Average IOU | 44.6 |
Medical Image Segmentation > Infant Brain Mri Segmentation | iSEG 2017 Challenge | LiviaNet (SemiDenseNet) | http://arxiv.org/abs/1712.05319v2 | Dice Score | 0.9243 |
Medical Image Segmentation > Acute Stroke Lesion Segmentation | ATLAS v2.0 | 2D U-Net Transformer | https://arxiv.org/abs/2310.07060v1 | Dice Score | 0.583 |
Medical Image Segmentation > Acute Stroke Lesion Segmentation | ATLAS v2.0 | 3D Residual U-Net | https://arxiv.org/abs/2310.07060v1 | Dice Score | 0.504 |
Medical Image Segmentation > Pulmorary Vessel Segmentation > Pulmonary Artery–Vein Classification | SunYs | CNN-GCNt | https://www.researchgate.net/publication/335620542_Linking_convolutional_neural_networks_with_graph_convolutional_networks_application_in_pulmonary_artery-vein_separation | Accuracy (median) | 0.778 |
Medical Image Segmentation > Pulmorary Vessel Segmentation > Pulmonary Artery–Vein Classification | SunYs | CNN-GCN | https://www.researchgate.net/publication/335620542_Linking_convolutional_neural_networks_with_graph_convolutional_networks_application_in_pulmonary_artery-vein_separation | Accuracy (median) | 0.764 |
Medical Image Segmentation > Pulmorary Vessel Segmentation > Pulmonary Artery–Vein Classification | SunYs | CNN3D | https://doi.org/10.1109/TMI.2018.2833385 | Accuracy (median) | 0.727 |
Medical Image Segmentation > Pulmorary Vessel Segmentation > Pulmonary Artery–Vein Classification | LUMC | CNN-GCNt | https://www.researchgate.net/publication/335620542_Linking_convolutional_neural_networks_with_graph_convolutional_networks_application_in_pulmonary_artery-vein_separation | Accuracy (median) | 0.738 |
Medical Image Segmentation > Pulmorary Vessel Segmentation > Pulmonary Artery–Vein Classification | LUMC | CNN-GCN | https://www.researchgate.net/publication/335620542_Linking_convolutional_neural_networks_with_graph_convolutional_networks_application_in_pulmonary_artery-vein_separation | Accuracy (median) | 0.723 |
Medical Image Segmentation > Pulmorary Vessel Segmentation > Pulmonary Artery–Vein Classification | LUMC | CNN3D | https://doi.org/10.1109/TMI.2018.2833385 | Accuracy (median) | 0.693 |
Image Generation | EMNIST-Letters | Spiking-Diffusion | https://arxiv.org/abs/2308.10187v4 | FID | 67.41 |
Image Generation | CLEVR | Projected GAN | https://arxiv.org/abs/2111.01007v1 | FID-5k-training-steps | 0.89 |
Image Generation | CLEVR | GANformer | https://arxiv.org/abs/2103.01209v4 | FID-5k-training-steps | 9.1679 |
Image Generation | CLEVR | StyleGAN2 | https://arxiv.org/abs/2103.01209v4 | FID-5k-training-steps | 16.0534 |
Image Generation | CLEVR | GAN | https://arxiv.org/abs/2103.01209v4 | FID-5k-training-steps | 25.0244 |
Image Generation | CLEVR | SAGAN | https://arxiv.org/abs/2103.01209v4 | FID-5k-training-steps | 26.0433 |
Image Generation | CLEVR | VQGAN | https://arxiv.org/abs/2103.01209v4 | FID-5k-training-steps | 32.6031 |
Image Generation | CUB 128 x 128 | Projected GAN | https://arxiv.org/abs/2111.01007v1 | FID | 2.79 |
Image Generation | CUB 128 x 128 | FineGAN | http://arxiv.org/abs/1811.11155v2 | FID | 11.25 |
Image Generation | CUB 128 x 128 | FineGAN | http://arxiv.org/abs/1811.11155v2 | Inception score | 52.53 |
Image Generation | CUB 128 x 128 | InfoGAN | http://arxiv.org/abs/1606.03657v1 | FID | 13.20 |
Image Generation | CUB 128 x 128 | InfoGAN | http://arxiv.org/abs/1606.03657v1 | Inception score | 47.32 |
Image Generation | CUB 128 x 128 | LR-GAN | http://arxiv.org/abs/1703.01560v3 | FID | 34.91 |
Image Generation | CUB 128 x 128 | LR-GAN | http://arxiv.org/abs/1703.01560v3 | Inception score | 13.50 |
Image Generation | Fashion-MNIST | GLF+perceptual loss (ours) | https://arxiv.org/abs/1905.10485v2 | FID | 10.3 |
Image Generation | Fashion-MNIST | Sliced Iterative Generator | https://arxiv.org/abs/2007.00674v3 | FID | 13.7 |
Image Generation | Fashion-MNIST | PeerGAN | https://arxiv.org/abs/2101.07524v3 | FID | 21.73 |
Image Generation | Fashion-MNIST | PAE | https://arxiv.org/abs/2006.05479v4 | FID | 28.0 |
Image Generation | Fashion-MNIST | PR-GLOW- Recall | null | FID | 42.85 |
Image Generation | Fashion-MNIST | PR-GLOW- Recall | null | Precision | 0.6648 |
Image Generation | Fashion-MNIST | PR-GLOW- Recall | null | Recall | 0.4973 |
Image Generation | Fashion-MNIST | PR-GLOW- Precision | null | FID | 83.25 |
Image Generation | Fashion-MNIST | PR-GLOW- Precision | null | Precision | 0.73 |
Image Generation | Fashion-MNIST | PR-GLOW- Precision | null | Recall | 0.34 |
Image Generation | Fashion-MNIST | Spiking-Diffusion | https://arxiv.org/abs/2308.10187v4 | FID | 91.98 |
Image Generation | LSUN Car 256 x 256 | StyleGAN2 | https://arxiv.org/abs/1912.04958v2 | FID | 2.32 |
Image Generation | STL-10 | Diffusion ProjectedGAN | https://arxiv.org/abs/2206.02262v4 | FID | 6.91 |
Image Generation | STL-10 | UNCSN++ (RVE) + ST | https://arxiv.org/abs/2106.05527v5 | FID | 7.71 |
Image Generation | STL-10 | UNCSN++ (RVE) + ST | https://arxiv.org/abs/2106.05527v5 | Inception score | 13.43 |
Image Generation | STL-10 | RDUOT | https://arxiv.org/abs/2311.17101v2 | FID | 11.5 |
Image Generation | STL-10 | RDUOT | https://arxiv.org/abs/2311.17101v2 | Recall | 0.49 |
Image Generation | STL-10 | Diffusion StyleGAN2 | https://arxiv.org/abs/2206.02262v4 | FID | 11.53 |
Image Generation | STL-10 | MMD-PMish-NAS | https://ieeexplore.ieee.org/document/10732016 | FID | 11.61 |
Image Generation | STL-10 | MMD-PMish-NAS | https://ieeexplore.ieee.org/document/10732016 | Inception score | 11.79 |
Image Generation | STL-10 | MMD-PMish-NAS | https://ieeexplore.ieee.org/document/10732016 | Model Size (MB) | 19.47 |
Image Generation | STL-10 | MMD-AdversarialNAS | https://ieeexplore.ieee.org/document/10446488 | FID | 12.91 |
Image Generation | STL-10 | MMD-AdversarialNAS | https://ieeexplore.ieee.org/document/10446488 | Inception score | 11.6 |
Image Generation | STL-10 | MMD-AdversarialNAS | https://ieeexplore.ieee.org/document/10446488 | Model Size (MB) | 19.47 |
Image Generation | STL-10 | WaveDiff | https://arxiv.org/abs/2211.16152v2 | FID | 12.93 |
Image Generation | STL-10 | WaveDiff | https://arxiv.org/abs/2211.16152v2 | Recall | 0.41 |
Image Generation | STL-10 | WaveDiff | https://arxiv.org/abs/2211.16152v2 | NFE | 4 |
Image Generation | STL-10 | MMD-AdversarialNAS (Compressed Large) | https://ieeexplore.ieee.org/document/10446488 | FID | 13.06 |
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