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 ⌀ |
|---|---|---|---|---|---|
Super-Resolution > Image Rescaling | DIV2K val-q30-4x | T-SAIN | https://arxiv.org/abs/2412.13508v1 | PSNR | 28.08 |
Super-Resolution > Image Rescaling | DIV2K val-q30-4x | T-SAIN | https://arxiv.org/abs/2412.13508v1 | SSIM | 0.7893 |
Super-Resolution > Image Rescaling | DIV2K val-q30-4x | SAIN | https://arxiv.org/abs/2303.02353v2 | PSNR | 27.90 |
Super-Resolution > Image Rescaling | DIV2K val-q30-4x | SAIN | https://arxiv.org/abs/2303.02353v2 | SSIM | 0.7745 |
Super-Resolution > Image Rescaling | DIV2K val-q30-4x | IRN | https://arxiv.org/abs/2005.05650v1 | PSNR | 25.98 |
Super-Resolution > Image Rescaling | DIV2K val-q30-4x | IRN | https://arxiv.org/abs/2005.05650v1 | SSIM | 0.6867 |
Super-Resolution > Image Rescaling | DIV2K val-q30-4x | HCFlow | https://arxiv.org/abs/2108.05301v1 | PSNR | 25.89 |
Super-Resolution > Image Rescaling | DIV2K val-q30-4x | HCFlow | https://arxiv.org/abs/2108.05301v1 | SSIM | 0.6838 |
Super-Resolution > Image Rescaling | BSD100-4x | T-IRN | https://arxiv.org/abs/2412.13508v1 | PSNR | 31.64 |
Super-Resolution > Image Rescaling | BSD100-4x | T-IRN | https://arxiv.org/abs/2412.13508v1 | SSIM | 0.8837 |
Super-Resolution > Image Rescaling | BSD100-4x | IRN | https://arxiv.org/abs/2005.05650v1 | PSNR | 31.64 |
Super-Resolution > Image Rescaling | BSD100-4x | IRN | https://arxiv.org/abs/2005.05650v1 | SSIM | 0.8826 |
Super-Resolution > Image Rescaling | DIV2K val-4x | T-IRN | https://arxiv.org/abs/2412.13508v1 | PSNR | 35.10 |
Super-Resolution > Image Rescaling | DIV2K val-4x | T-IRN | https://arxiv.org/abs/2412.13508v1 | SSIM | 0.9328 |
Super-Resolution > Image Rescaling | DIV2K val-4x | IRN | https://arxiv.org/abs/2005.05650v1 | PSNR | 35.07 |
Super-Resolution > Image Rescaling | DIV2K val-4x | IRN | https://arxiv.org/abs/2005.05650v1 | SSIM | 0.9318 |
Super-Resolution > Image Rescaling | Set14-4x | T-IRN | https://arxiv.org/abs/2412.13508v1 | PSNR | 32.70 |
Super-Resolution > Image Rescaling | Set14-4x | T-IRN | https://arxiv.org/abs/2412.13508v1 | SSIM | 0.9003 |
Super-Resolution > Image Rescaling | Set14-4x | IRN | https://arxiv.org/abs/2005.05650v1 | PSNR | 32.67 |
Super-Resolution > Image Rescaling | Set14-4x | IRN | https://arxiv.org/abs/2005.05650v1 | SSIM | 0.9015 |
Super-Resolution > Image Rescaling | DIV2K val-q90-4x | T-SAIN | https://arxiv.org/abs/2412.13508v1 | PSNR | 30.92 |
Super-Resolution > Image Rescaling | DIV2K val-q90-4x | T-SAIN | https://arxiv.org/abs/2412.13508v1 | SSIM | 0.8517 |
Super-Resolution > Image Rescaling | DIV2K val-q90-4x | SAIN | https://arxiv.org/abs/2303.02353v2 | PSNR | 30.31 |
Super-Resolution > Image Rescaling | DIV2K val-q90-4x | SAIN | https://arxiv.org/abs/2303.02353v2 | SSIM | 0.8367 |
Super-Resolution > Image Rescaling | DIV2K val-q90-4x | IRN | https://arxiv.org/abs/2005.05650v1 | PSNR | 28.42 |
Super-Resolution > Image Rescaling | DIV2K val-q90-4x | IRN | https://arxiv.org/abs/2005.05650v1 | SSIM | 0.7777 |
Super-Resolution > Image Rescaling | DIV2K val-q90-4x | HCFlow | https://arxiv.org/abs/2108.05301v1 | PSNR | 27.41 |
Super-Resolution > Image Rescaling | DIV2K val-q90-4x | HCFlow | https://arxiv.org/abs/2108.05301v1 | SSIM | 0.7485 |
Super-Resolution > Image Rescaling | Urban100-4x | IRN | https://arxiv.org/abs/2005.05650v1 | PSNR | 31.41 |
Super-Resolution > Image Rescaling | Urban100-4x | IRN | https://arxiv.org/abs/2005.05650v1 | SSIM | 0.9157 |
Super-Resolution > Image Rescaling | Urban100-4x | T-IRN | https://arxiv.org/abs/2412.13508v1 | PSNR | 31.19 |
Super-Resolution > Image Rescaling | Urban100-4x | T-IRN | https://arxiv.org/abs/2412.13508v1 | SSIM | 0.9132 |
Super-Resolution > Image Rescaling | Set14-2x | T-IRN | https://arxiv.org/abs/2412.13508v1 | PSNR | 41.70 |
Super-Resolution > Image Rescaling | Set14-2x | T-IRN | https://arxiv.org/abs/2412.13508v1 | SSIM | 0.9809 |
Super-Resolution > Image Rescaling | Set14-2x | IRN | https://arxiv.org/abs/2005.05650v1 | PSNR | 40.79 |
Super-Resolution > Image Rescaling | Set14-2x | IRN | https://arxiv.org/abs/2005.05650v1 | SSIM | 0.9778 |
Super-Resolution > Image Rescaling | DIV2K val-q50-2x | T-SAIN | https://arxiv.org/abs/2412.13508v1 | PSNR | 33.71 |
Super-Resolution > Image Rescaling | DIV2K val-q50-2x | T-SAIN | https://arxiv.org/abs/2412.13508v1 | SSIM | 0.9210 |
Super-Resolution > Image Rescaling | DIV2K val-q50-2x | SAIN | https://arxiv.org/abs/2303.02353v2 | PSNR | 33.17 |
Super-Resolution > Image Rescaling | DIV2K val-q50-2x | SAIN | https://arxiv.org/abs/2303.02353v2 | SSIM | 0.9082 |
Super-Resolution > Image Rescaling | DIV2K val-q50-2x | IRN | https://arxiv.org/abs/2005.05650v1 | PSNR | 30.20 |
Super-Resolution > Image Rescaling | DIV2K val-q50-2x | IRN | https://arxiv.org/abs/2005.05650v1 | SSIM | 0.8342 |
Super-Resolution > Image Rescaling | DIV2K val-q50-4x | T-SAIN | https://arxiv.org/abs/2412.13508v1 | PSNR | 29.43 |
Super-Resolution > Image Rescaling | DIV2K val-q50-4x | T-SAIN | https://arxiv.org/abs/2412.13508v1 | SSIM | 0.8237 |
Super-Resolution > Image Rescaling | DIV2K val-q50-4x | SAIN | https://arxiv.org/abs/2303.02353v2 | PSNR | 29.05 |
Super-Resolution > Image Rescaling | DIV2K val-q50-4x | SAIN | https://arxiv.org/abs/2303.02353v2 | SSIM | 0.8088 |
Super-Resolution > Image Rescaling | DIV2K val-q50-4x | IRN | https://arxiv.org/abs/2005.05650v1 | PSNR | 26.62 |
Super-Resolution > Image Rescaling | DIV2K val-q50-4x | IRN | https://arxiv.org/abs/2005.05650v1 | SSIM | 0.7096 |
Super-Resolution > Image Rescaling | DIV2K val-q50-4x | HCFlow | https://arxiv.org/abs/2108.05301v1 | PSNR | 26.38 |
Super-Resolution > Image Rescaling | DIV2K val-q50-4x | HCFlow | https://arxiv.org/abs/2108.05301v1 | SSIM | 0.7029 |
Super-Resolution > Reference-based Video Super-Resolution | RealMCVSR | RefVSR-IR-ℓ1 | https://arxiv.org/abs/2203.14537v1 | PSNR | 34.86 |
Super-Resolution > Reference-based Video Super-Resolution | RealMCVSR | RefVSR-ℓ1 | https://arxiv.org/abs/2203.14537v1 | PSNR | 34.74 |
Super-Resolution > Reference-based Video Super-Resolution | RealMCVSR | RefVSR-small-ℓ1 | https://arxiv.org/abs/2203.14537v1 | PSNR | 33.88 |
Super-Resolution > Reference-based Video Super-Resolution | RealMCVSR | IconVSR-ℓch [chan2021basicvsr] | https://arxiv.org/abs/2203.14537v1 | PSNR | 33.80 |
Super-Resolution > Reference-based Video Super-Resolution | RealMCVSR | BasicVSR-ℓch [chan2021basicvsr] | https://arxiv.org/abs/2203.14537v1 | PSNR | 33.66 |
Super-Resolution > Reference-based Video Super-Resolution | RealMCVSR | EDVR-ℓch [wang2019edvr] | https://arxiv.org/abs/2203.14537v1 | PSNR | 33.47 |
Super-Resolution > Reference-based Video Super-Resolution | RealMCVSR | EDVR-M-ℓch [wang2019edvr] | https://arxiv.org/abs/2203.14537v1 | PSNR | 33.26 |
Super-Resolution > Reference-based Video Super-Resolution | RealMCVSR | DCSR-ℓ1 [wang2021DCSR] | https://arxiv.org/abs/2203.14537v1 | PSNR | 32.43 |
Super-Resolution > Reference-based Video Super-Resolution | RealMCVSR | RCAN-ℓ1 [zhang2018rcan] | https://arxiv.org/abs/2203.14537v1 | PSNR | 31.07 |
Super-Resolution > Reference-based Video Super-Resolution | RealMCVSR | TTSR-ℓ1 [yang2020TTSR] | https://arxiv.org/abs/2203.14537v1 | PSNR | 30.83 |
Electroencephalogram (EEG) | HS-SSVEP | MultitaskSSVEP | https://ieeexplore.ieee.org/abstract/document/9283310 | Accuracy (5-fold) | 92.2 |
Electroencephalogram (EEG) | SEED | DBN | https://doi.org/10.1109/TAMD.2015.2431497 | Accuracy | 86.08 |
Electroencephalogram (EEG) | SEED-IV | BiHDM | http://arxiv.org/abs/1906.01704v1 | Accuracy | 74.35 |
Electroencephalogram (EEG) | SEED-IV | DGCNN | https://doi.org/10.1109/taffc.2018.2817622 | Accuracy | 69.88 |
Electroencephalogram (EEG) | SEED-IV | DBN | https://doi.org/10.1109/TAMD.2015.2431497 | Accuracy | 66.77 |
Electroencephalogram (EEG) > Eeg Decoding | CWL EEG/fMRI Dataset | BEIRA | https://arxiv.org/abs/2211.02024v2 | Pearson Correlation | 0.44 |
Electroencephalogram (EEG) > Eeg Decoding > EEG Signal Classification | . | Bipolar Neural Network | https://www.researchgate.net/publication/309967859_Imagined_Speech_Classification_using_EEG | Accuracy (% ) | 44 |
Electroencephalogram (EEG) > Attention Score Prediction | PhyAAt | SVM | https://arxiv.org/abs/2005.11577v1 | MAE | 29.65 |
Electroencephalogram (EEG) > Noise Level Prediction | PhyAAt | SVM | https://arxiv.org/abs/2005.11577v1 | MAE | 4.75 |
Electroencephalogram (EEG) > Semanticity prediction | PhyAAt | SVM | https://arxiv.org/abs/2005.11577v1 | Accuracy | 56 |
Electroencephalogram (EEG) > Semanticity prediction | VizNet | DCoM-Single-DistilBERT | https://arxiv.org/abs/2106.12871v1 | F1 score | 0.925 |
Electroencephalogram (EEG) > LWR Classification | PhyAAt | SVM | https://arxiv.org/abs/2005.11577v1 | Accuracy | 81 |
X-Ray > Low-Dose X-Ray Ct Reconstruction | X3D | SAX-NeRF | https://arxiv.org/abs/2311.10959v3 | PSNR | 37.25 |
X-Ray > Low-Dose X-Ray Ct Reconstruction | X3D | SAX-NeRF | https://arxiv.org/abs/2311.10959v3 | SSIM | 0.9753 |
X-Ray > Low-Dose X-Ray Ct Reconstruction | X3D | NAF | https://arxiv.org/abs/2209.14540v1 | PSNR | 34.76 |
X-Ray > Low-Dose X-Ray Ct Reconstruction | X3D | NAF | https://arxiv.org/abs/2209.14540v1 | SSIM | 0.9535 |
X-Ray > Low-Dose X-Ray Ct Reconstruction | X3D | TensoRF | https://arxiv.org/abs/2203.09517v2 | PSNR | 33.78 |
X-Ray > Low-Dose X-Ray Ct Reconstruction | X3D | TensoRF | https://arxiv.org/abs/2203.09517v2 | SSIM | 0.9387 |
X-Ray > Low-Dose X-Ray Ct Reconstruction | X3D | NeAT | https://arxiv.org/abs/2202.02171v1 | PSNR | 33.41 |
X-Ray > Low-Dose X-Ray Ct Reconstruction | X3D | NeAT | https://arxiv.org/abs/2202.02171v1 | SSIM | 0.9447 |
X-Ray > Low-Dose X-Ray Ct Reconstruction | X3D | SART | https://arxiv.org/abs/2311.10959v3 | PSNR | 32.33 |
X-Ray > Low-Dose X-Ray Ct Reconstruction | X3D | SART | https://arxiv.org/abs/2311.10959v3 | SSIM | 0.9342 |
X-Ray > Low-Dose X-Ray Ct Reconstruction | X3D | ASD-POCS | https://arxiv.org/abs/2311.10959v3 | PSNR | 32.32 |
X-Ray > Low-Dose X-Ray Ct Reconstruction | X3D | ASD-POCS | https://arxiv.org/abs/2311.10959v3 | SSIM | 0.9400 |
X-Ray > Low-Dose X-Ray Ct Reconstruction | X3D | NeRF | https://arxiv.org/abs/2003.08934v2 | PSNR | 32.15 |
X-Ray > Low-Dose X-Ray Ct Reconstruction | X3D | NeRF | https://arxiv.org/abs/2003.08934v2 | SSIM | 0.9354 |
X-Ray > Low-Dose X-Ray Ct Reconstruction | X3D | InTomo | http://openaccess.thecvf.com//content/ICCV2021/html/Zang_IntraTomo_Self-Supervised_Learning-Based_Tomography_via_Sinogram_Synthesis_and_Prediction_ICCV_2021_paper.html | PSNR | 30.29 |
X-Ray > Low-Dose X-Ray Ct Reconstruction | X3D | InTomo | http://openaccess.thecvf.com//content/ICCV2021/html/Zang_IntraTomo_Self-Supervised_Learning-Based_Tomography_via_Sinogram_Synthesis_and_Prediction_ICCV_2021_paper.html | SSIM | 0.9189 |
X-Ray > Low-Dose X-Ray Ct Reconstruction | X3D | FDK | https://arxiv.org/abs/2311.10959v3 | PSNR | 25.12 |
X-Ray > Low-Dose X-Ray Ct Reconstruction | X3D | FDK | https://arxiv.org/abs/2311.10959v3 | SSIM | 0.6422 |
Cancer > Breast Cancer Detection | Breast Cancer Coimbra Data Set | CFS-TSK+ | https://arxiv.org/abs/2101.03581v3 | Mean Accuracy | 79.17 |
Cancer > Breast Cancer Detection | Breast cancer Wisconsin_class 4 | XBNET | https://arxiv.org/abs/2106.05239v3 | Accuracy | 96.49 |
Cancer > Breast Cancer Detection | Breast cancer Wisconsin_class 4 | XBNET | https://arxiv.org/abs/2106.05239v3 | Average Precision | 0.95 |
Cancer > Breast Cancer Detection | CMMD | Luminal vs Non Luminal | https://arxiv.org/abs/2301.09282v1 | AUC | 0.6688 |
Cancer > Breast Cancer Detection | BreakHis | IRv2-CXL | https://arxiv.org/abs/2209.01380v1 | 1:1 Accuracy | 96.46 |
Cancer > Breast Cancer Detection | BreakHis | Breast-NET | https://link.springer.com/article/10.1007/s00521-024-10298-9 | 1:1 Accuracy | 98.11 |
Cancer > Lung Cancer Diagnosis | National Lung Screening Trial (NLST) | ResNet50 SWS++ | https://arxiv.org/abs/2405.04605v2 | AUC | 0.81 (0.79-0.82) |
Cancer > Lung Cancer Diagnosis | Duke Lung Nodule Dataset 2024 | ResNet50 SWS++ | https://arxiv.org/abs/2405.04605v2 | AUC | 0.71 ± 0.10 |
Cancer > Skin Cancer Classification | ISIC 2017 | VGG19 | https://arxiv.org/abs/2212.05116v3 | Accuracy Improvement | 17% |
Cancer > Breast Cancer Histology Image Classification | ICIAR 2018 Grand Challenge on Breast Cancer Histology Images | ResNet-152 | https://www.researchgate.net/publication/374471906_Breast_cancer_histology_classification_using_Deep_Residual_Networks | Accuracy (% ) | 83 |
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