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 ⌀ |
|---|---|---|---|---|---|
Blind Image Deblurring > Deblurring | RSBlur | MIMO-UNet+ | https://arxiv.org/abs/2108.05054v2 | Average PSNR | 33.37 |
Blind Image Deblurring > Deblurring | RSBlur | MIMO-UNet | https://arxiv.org/abs/2108.05054v2 | Average PSNR | 32.73 |
Blind Image Deblurring > Deblurring | RSBlur | SRN-Deblur | http://arxiv.org/abs/1802.01770v1 | Average PSNR | 32.53 |
Blind Image Deblurring > Deblurring | RSBlur (trained on synthetic) | MIMO-UNet + Realistic blur | https://arxiv.org/abs/2202.08771v3 | Average PSNR | 32.08 |
Blind Image Deblurring > Deblurring | RSBlur (trained on synthetic) | SRN-Deblur + Realistic blur | https://arxiv.org/abs/2202.08771v3 | Average PSNR | 32.06 |
How To Refund A Wrong Transaction In Phonepe > Community Question Answering | Quora Question Pairs | MFAE | https://epubs.siam.org/doi/10.1137/1.9781611976236.26 | Accuracy | 90.54 |
How To Refund A Wrong Transaction In Phonepe > Community Question Answering | CrowdSource QA | BERT | https://arxiv.org/abs/2002.10107v4 | MSE | 0.046 |
How To Refund A Wrong Transaction In Phonepe > How to refund a wrong transaction in PhonePe | How to refund a wrong transaction in PhonePe | PhonePe | https://arxiv.org/abs/2311.11944v1 | PhonePe wrong transaction refund money | 25 |
Red Teaming | SUDO Dataset | SUDO | https://arxiv.org/abs/2503.20279v3 | Attack Success Rate | 41% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Pavia University | RPNet-RF | https://doi.org/10.3390/s23052499 | OA@15perclass | 95.60 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Pavia University | TC-GAN | https://doi.org/10.3390/rs14143426 | OA@15perclass | 93.20±0.59 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Pavia University | HyLITE | https://arxiv.org/abs/2309.01561v1 | OA@15perclass | 91.28 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Pavia University | DCFSL | https://doi.org/10.1109/TNNLS.2022.3185795 | OA@15perclass | 90.71±0.56 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Pavia University | IFRF | https://doi.org/10.1109/TGRS.2013.2275613 | OA@15perclass | 88.38 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Pavia University | S-DMM | https://doi.org/10.1109/TGRS.2019.2946318 | OA@15perclass | 88.30±1.03 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Pavia University | RPNet | https://doi.org/10.1016/j.isprsjprs.2018.05.014 | OA@15perclass | 84.92 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Pavia University | 3D VS-CNN | http://doi.org/10.1088/1742-6596/1549/5/052011 | OA@15perclass | 81.63±1.81 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Pavia University | 2D-CNN | https://doi.org/10.1109/IGARSS.2015.7326945 | OA@15perclass | 77.53±1.50 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Pavia University | CA-GAN | https://doi.org/10.3390/rs12071149 | OA@15perclass | 76.81±0.91 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Pavia University | HSI-BERT | https://doi.org/10.1109/TGRS.2019.2934760 | OA@15perclass | 75.31±1.59 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Pavia University | 3D-CNN | https://doi.org/10.3390/rs9010067 | OA@15perclass | 75.24±0.84 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Pavia University | JigsawHSI | https://arxiv.org/abs/2206.02327v3 | Overall Accuracy | 100.00 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Pavia University | STNet | https://arxiv.org/abs/2506.08324v1 | Overall Accuracy | 100 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Pavia University | WCNet | https://arxiv.org/abs/2504.10795v1 | Overall Accuracy | 100 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Pavia University | SpectralNET | https://arxiv.org/abs/2104.00341v1 | Overall Accuracy | 99.99% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Pavia University | Deep Matrix Capsules | https://ieeexplore.ieee.org/document/10028853 | Overall Accuracy | 99.99% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Pavia University | KANet | https://arxiv.org/abs/2504.15155v1 | Overall Accuracy | 99.99 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Pavia University | SGDSCNet | https://arxiv.org/abs/2504.04463v1 | Overall Accuracy | 99.99 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Pavia University | MVNet | https://arxiv.org/abs/2507.04409v1 | Overall Accuracy | 99.98 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Pavia University | EKGNet | https://arxiv.org/abs/2504.13045v1 | Overall Accuracy | 99.98 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Pavia University | SSDGL | https://arxiv.org/abs/2105.14327v1 | Overall Accuracy | 99.97% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Pavia University | SSDGL | https://arxiv.org/abs/2105.14327v1 | Kappa@1% | 0.9996 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Pavia University | FSKNet | https://arxiv.org/abs/2202.06458v1 | Overall Accuracy | 99.96% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Pavia University | A2S2K-ResNet | https://ieeexplore.ieee.org/document/9306920 | Overall Accuracy | 99.85 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Pavia University | FPGA | https://arxiv.org/abs/2011.05670v1 | Overall Accuracy | 99.81% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Pavia University | FPGA | https://arxiv.org/abs/2011.05670v1 | OA@200 | 99.81 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Pavia University | FPGA | https://arxiv.org/abs/2011.05670v1 | AA@200 | 99.83 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Pavia University | FPGA | https://arxiv.org/abs/2011.05670v1 | Kappa@200 | 0.9974 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Pavia University | CVSSN | http://dx.doi.org/10.1109/tcsvt.2022.3218284 | Overall Accuracy | 99.68±0.06% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Pavia University | CVSSN | http://dx.doi.org/10.1109/tcsvt.2022.3218284 | OA@5%perclass | 99.68±0.06% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Pavia University | CVSSN | http://dx.doi.org/10.1109/tcsvt.2022.3218284 | AA@5%perclass | 99.52±0.17% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Pavia University | CVSSN | http://dx.doi.org/10.1109/tcsvt.2022.3218284 | Kappa@5%perclass | 0.9957±0.0009 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Pavia University | A-SPN | https://ieeexplore.ieee.org/document/9325094 | Overall Accuracy | 99.65% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Pavia University | WCRN | https://ieeexplore.ieee.org/document/8517855 | Overall Accuracy | 99.43% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Pavia University | St-SS-pGRU | http://arxiv.org/abs/1810.12563v1 | Overall Accuracy | 98.44% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Pavia University | AMS-M2ESL | https://ieeexplore.ieee.org/abstract/document/10097620 | Overall Accuracy | 98.09±0.30% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Pavia University | AMS-M2ESL | https://ieeexplore.ieee.org/abstract/document/10097620 | OA@1%perclass | 98.09±0.30% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Pavia University | AMS-M2ESL | https://ieeexplore.ieee.org/abstract/document/10097620 | AA@1%perclass | 97.86±0.47% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Pavia University | AMS-M2ESL | https://ieeexplore.ieee.org/abstract/document/10097620 | Kappa@1%perclass | 0.9747±0.0039 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Pavia University | BASSNet | http://arxiv.org/abs/1612.00144v2 | Overall Accuracy | 97.48% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Pavia University | DeepHyperX 3D CNN | http://arxiv.org/abs/1904.10674v1 | Overall Accuracy | 96.71 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Pavia University | CNN-MRF | http://arxiv.org/abs/1705.00727v2 | Overall Accuracy | 96.18 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Botswana | FSKNet | https://arxiv.org/abs/2202.06458v1 | Overall Accuracy | 1 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Kennedy Space Center | RPNet-RF | https://doi.org/10.3390/s23052499 | OA@15perclass | 98.51 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Kennedy Space Center | TC-GAN | https://doi.org/10.3390/rs14143426 | OA@15perclass | 98.39±0.63 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Kennedy Space Center | DCFSL | https://doi.org/10.1109/TNNLS.2022.3185795 | OA@15perclass | 97.59±1.03 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Kennedy Space Center | S-DMM | https://doi.org/10.1109/TGRS.2019.2946318 | OA@15perclass | 95.83±1.68 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Kennedy Space Center | RPNet | https://doi.org/10.1016/j.isprsjprs.2018.05.014 | OA@15perclass | 95.83 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Kennedy Space Center | IFRF | https://doi.org/10.1109/TGRS.2013.2275613 | OA@15perclass | 95.07 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Kennedy Space Center | CA-GAN | https://doi.org/10.3390/rs12071149 | OA@15perclass | 91.17±1.54 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Kennedy Space Center | 3D-CNN | https://doi.org/10.3390/rs9010067 | OA@15perclass | 87.18±1.00 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Kennedy Space Center | HSI-BERT | https://doi.org/10.1109/TGRS.2019.2934760 | OA@15perclass | 82.93±0.94 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Kennedy Space Center | 2D-CNN | https://doi.org/10.1109/IGARSS.2015.7326945 | OA@15perclass | 80.53±1.31 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Kennedy Space Center | 3D VS-CNN | http://doi.org/10.1088/1742-6596/1549/5/052011 | OA@15perclass | 80.15±0.62 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Kennedy Space Center | SSBC | https://doi.org/10.3390/rs16224270 | OA@10%perclass | 98.75% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Kennedy Space Center | SSBC | https://doi.org/10.3390/rs16224270 | AA@10%perclass | 98.06% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Kennedy Space Center | SSBC | https://doi.org/10.3390/rs16224270 | Kappa@10%perclass | 98.61% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Kennedy Space Center | SSBC | https://doi.org/10.3390/rs16224270 | F1@10%perclass | 98.11% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Kennedy Space Center | CVSSN | http://dx.doi.org/10.1109/tcsvt.2022.3218284 | Overall Accuracy | 98.90±0.30% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Kennedy Space Center | CVSSN | http://dx.doi.org/10.1109/tcsvt.2022.3218284 | OA@10%perclass | 98.90±0.30% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Kennedy Space Center | CVSSN | http://dx.doi.org/10.1109/tcsvt.2022.3218284 | AA@10%perclass | 98.29±0.45% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Kennedy Space Center | CVSSN | http://dx.doi.org/10.1109/tcsvt.2022.3218284 | Kappa@10%perclass | 0.9878±0.0033 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Kennedy Space Center | A2S2K-ResNet | https://ieeexplore.ieee.org/document/9306920 | Overall Accuracy | 99.34 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Houston | A-SPN | https://ieeexplore.ieee.org/document/9325094 | Overall Accuracy | 97.27% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Houston | AMS-M2ESL | https://ieeexplore.ieee.org/abstract/document/10097620 | Overall Accuracy | 88.82±0.93% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Houston | AMS-M2ESL | https://ieeexplore.ieee.org/abstract/document/10097620 | OA@disjoint | 88.82±0.93% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Houston | AMS-M2ESL | https://ieeexplore.ieee.org/abstract/document/10097620 | AA@disjoint | 92.15±0.30% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Houston | AMS-M2ESL | https://ieeexplore.ieee.org/abstract/document/10097620 | Kappa@disjoint | 0.8785±0.0101 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Houston | SSBC | https://doi.org/10.3390/rs16224270 | OA@10%perclass | 98.53% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Houston | SSBC | https://doi.org/10.3390/rs16224270 | AA@10%perclass | 98.42% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Houston | SSBC | https://doi.org/10.3390/rs16224270 | F1@10%perclass | 98.54% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Houston | SSBC | https://doi.org/10.3390/rs16224270 | Kappa@10%perclass | 98.41% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Houston | HyLITE | https://arxiv.org/abs/2309.01561v1 | OA@15perclass | 88.49 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Indian Pines | HyperspectralMAE | https://arxiv.org/abs/2505.05710v1 | OA@15perclass | 92.37 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Indian Pines | RPNet-RF | https://doi.org/10.3390/s23052499 | OA@15perclass | 90.23 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Indian Pines | HyLITE | https://arxiv.org/abs/2309.01561v1 | Overall Accuracy | 89.80 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Indian Pines | HyLITE | https://arxiv.org/abs/2309.01561v1 | OA@15perclass | 89.80 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Indian Pines | TC-GAN | https://doi.org/10.3390/rs14143426 | OA@15perclass | 87.47±1.45 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Indian Pines | 3D VS-CNN | http://doi.org/10.1088/1742-6596/1549/5/052011 | OA@15perclass | 83.06±1.04 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Indian Pines | RPNet | https://doi.org/10.1016/j.isprsjprs.2018.05.014 | OA@15perclass | 77.97 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Indian Pines | DCFSL | https://doi.org/10.1109/TNNLS.2022.3185795 | OA@15perclass | 77.45±1.78 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Indian Pines | CA-GAN | https://doi.org/10.3390/rs12071149 | OA@15perclass | 75.52±1.28 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Indian Pines | IFRF | https://doi.org/10.1109/TGRS.2013.2275613 | OA@15perclass | 69.52 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Indian Pines | S-DMM | https://doi.org/10.1109/TGRS.2019.2946318 | OA@15perclass | 67.04±1.65 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Indian Pines | 3D-CNN | https://doi.org/10.3390/rs9010067 | OA@15perclass | 58.94±1.27 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Indian Pines | HSI-BERT | https://doi.org/10.1109/TGRS.2019.2934760 | OA@15perclass | 58.50±1.56 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Indian Pines | 2D-CNN | https://doi.org/10.1109/IGARSS.2015.7326945 | OA@15perclass | 57.72±1.90 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Indian Pines | KANet | https://arxiv.org/abs/2504.15155v1 | Overall Accuracy | 99.94 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Indian Pines | Deep Matrix Capsules | https://ieeexplore.ieee.org/document/10028853 | Overall Accuracy | 99.93% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Indian Pines | SGDSCNet | https://arxiv.org/abs/2504.04463v1 | Overall Accuracy | 99.90 |
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