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 ⌀ |
|---|---|---|---|---|---|
Domain Adaptation | Synth Objects-to-LINEMOD | DSN (DANN) | http://arxiv.org/abs/1608.06019v1 | Mean Angle Error | 53.27 |
Domain Adaptation | Cityscapes-to-FoggyDriving | CoDA | https://arxiv.org/abs/2403.17369v3 | mIoU | 61.0 |
Domain Adaptation | Cityscapes-to-FoggyDriving | BWG | https://pure.uva.nl/ws/files/153124325/2357.pdf | mIoU | 54.2 |
Domain Adaptation | Cityscapes-to-FoggyDriving | FogAdapt+ | https://arxiv.org/abs/2201.02588v3 | mIoU | 53.4 |
Domain Adaptation | Cityscapes-to-FoggyDriving | FIFO | https://arxiv.org/abs/2204.01587v1 | mIoU | 50.7 |
Domain Adaptation | Cityscapes-to-FoggyDriving | CMAda3+ | http://arxiv.org/abs/1901.01415v2 | mIoU | 49.8 |
Domain Adaptation | GTAV+Synscapes to Cityscapes | DDB | https://arxiv.org/abs/2209.07695v3 | mIoU | 69.0 |
Domain Adaptation | GTAV+Synscapes to Cityscapes | MSCL | https://arxiv.org/abs/2103.04717v3 | mIoU | 59.0 |
Domain Adaptation | GTAV+Synscapes to Cityscapes | MADAN | https://arxiv.org/abs/1910.12181v1 | mIoU | 55.7 |
Domain Adaptation | GTAV+Synscapes to Cityscapes | MDAN | http://papers.nips.cc/paper/8075-adversarial-multiple-source-domain-adaptation | mIoU | 55.2 |
Domain Adaptation | GTAV+Synscapes to Cityscapes | MRNet+Adaboost | https://arxiv.org/abs/2103.15685v3 | mIoU | 50.8 |
Domain Adaptation | GTAV+Synscapes to Cityscapes | MRNet | https://arxiv.org/abs/1912.11164v3 | mIoU | 47.6 |
Domain Adaptation | TuLane | UFLD-SGPCS-ResNet32 | https://arxiv.org/abs/2206.08083v4 | Lane Accuracy (LA) | 93.29 |
Domain Adaptation | TuLane | UFLD-SGADA-ResNet32 | https://arxiv.org/abs/2206.08083v4 | Lane Accuracy (LA) | 92.04 |
Domain Adaptation | TuLane | UFLD-SGADA-ResNet18 | https://arxiv.org/abs/2206.08083v4 | Lane Accuracy (LA) | 91.70 |
Domain Adaptation | TuLane | UFLD-SGPCS-ResNet18 | https://arxiv.org/abs/2206.08083v4 | Lane Accuracy (LA) | 91.55 |
Domain Adaptation | TuLane | UFLD-ADDA-ResNet32 | https://arxiv.org/abs/2206.08083v4 | Lane Accuracy (LA) | 91.39 |
Domain Adaptation | TuLane | UFLD-DANN-ResNet32 | https://arxiv.org/abs/2206.08083v4 | Lane Accuracy (LA) | 91.06 |
Domain Adaptation | TuLane | UFLD-ADDA-ResNet18 | https://arxiv.org/abs/2206.08083v4 | Lane Accuracy (LA) | 90.72 |
Domain Adaptation | TuLane | UFLD-DANN-ResNet18 | https://arxiv.org/abs/2206.08083v4 | Lane Accuracy (LA) | 88.74 |
Domain Adaptation | SVNH-to-MNIST | SRDA (RAN) | https://arxiv.org/abs/1905.10748v4 | Accuracy | 98.91 |
Domain Adaptation | SVNH-to-MNIST | SHOT | https://arxiv.org/abs/2002.08546v6 | Accuracy | 98.9 |
Domain Adaptation | SVNH-to-MNIST | rRevGrad+CAT | https://arxiv.org/abs/1903.09980v2 | Accuracy | 98.8 |
Domain Adaptation | SVNH-to-MNIST | dSNE | http://openaccess.thecvf.com/content_CVPR_2019/html/Xu_d-SNE_Domain_Adaptation_Using_Stochastic_Neighborhood_Embedding_CVPR_2019_paper.html | Accuracy | 97.60 |
Domain Adaptation | SVNH-to-MNIST | DeepJDOT | http://arxiv.org/abs/1803.10081v3 | Accuracy | 96.7 |
Domain Adaptation | SVNH-to-MNIST | 3CATN | https://arxiv.org/abs/1909.07618v1 | Accuracy | 92.5 |
Domain Adaptation | SVNH-to-MNIST | DSN (DANN) | http://arxiv.org/abs/1608.06019v1 | Accuracy | 82.7 |
Domain Adaptation | SVNH-to-MNIST | MMD [tzeng2015ddc]; [long2015learning] | http://arxiv.org/abs/1502.02791v2 | Accuracy | 71.1 |
Domain Adaptation | SVNH-to-MNIST | DANN [ganin2016domain] | http://arxiv.org/abs/1505.07818v4 | Accuracy | 70.7 |
Domain Adaptation | MoLane | UFLD-SGPCS-ResNet18 | https://arxiv.org/abs/2206.08083v4 | Lane Accuracy (LA) | 93.94 |
Domain Adaptation | MoLane | UFLD-SGADA-ResNet18 | https://arxiv.org/abs/2206.08083v4 | Lane Accuracy (LA) | 93.82 |
Domain Adaptation | MoLane | UFLD-SGPCS-ResNet32 | https://arxiv.org/abs/2206.08083v4 | Lane Accuracy (LA) | 93.53 |
Domain Adaptation | MoLane | UFLD-SGADA-ResNet32 | https://arxiv.org/abs/2206.08083v4 | Lane Accuracy (LA) | 93.31 |
Domain Adaptation | MoLane | UFLD-ADDA-ResNet18 | https://arxiv.org/abs/2206.08083v4 | Lane Accuracy (LA) | 92.85 |
Domain Adaptation | MoLane | UFLD-ADDA-ResNet32 | https://arxiv.org/abs/2206.08083v4 | Lane Accuracy (LA) | 92.39 |
Domain Adaptation | MoLane | UFLD-DANN-ResNet32 | https://arxiv.org/abs/2206.08083v4 | Lane Accuracy (LA) | 90.91 |
Domain Adaptation | MoLane | UFLD-DANN-ResNet18 | https://arxiv.org/abs/2206.08083v4 | Lane Accuracy (LA) | 87.65 |
Domain Adaptation | UCF-to-Olympic | TA3N | https://arxiv.org/abs/1905.10861v5 | Accuracy | 98.15 |
Domain Adaptation | UCF-to-Olympic | TemPooling + RevGrad | http://arxiv.org/abs/1409.7495v2 | Accuracy | 98.15 |
Domain Adaptation | UCF-to-Olympic | W. Sultani et al. | http://openaccess.thecvf.com/content_cvpr_2014/html/Sultani_Human_Action_Recognition_2014_CVPR_paper.html | Accuracy | 33.33 |
Domain Adaptation | LeukemiaAttri | ConfMix [23] L_100x_C2 | https://arxiv.org/abs/2405.10803v1 | mAP | 33.5 |
Domain Adaptation | HMDBfull-to-UCF | UNITE | https://arxiv.org/abs/2312.02914v4 | Accuracy | 92.5 |
Domain Adaptation | HMDBfull-to-UCF | TA3N | https://arxiv.org/abs/1905.10861v5 | Accuracy | 81.79 |
Domain Adaptation | HMDBfull-to-UCF | JAN | http://arxiv.org/abs/1605.06636v2 | Accuracy | 79.69 |
Domain Adaptation | HMDBfull-to-UCF | MCD | http://arxiv.org/abs/1712.02560v4 | Accuracy | 79.34 |
Domain Adaptation | HMDBfull-to-UCF | RevGrad | http://arxiv.org/abs/1409.7495v2 | Accuracy | 74.44 |
Domain Adaptation | Office-Caltech | SPL | https://arxiv.org/abs/1911.07982v1 | Average Accuracy | 93 |
Domain Adaptation | Office-Caltech | MEDA[[Wang et al.2018]] | http://arxiv.org/abs/1807.07258v2 | Average Accuracy | 92.8 |
Domain Adaptation | Office-Caltech | CAPLS [[Wang, Bu, and Breckon2019]] | https://arxiv.org/abs/1903.10601v2 | Average Accuracy | 91.8 |
Domain Adaptation | Office-Caltech | DAN[[Long et al.2015]] | http://arxiv.org/abs/1502.02791v2 | Average Accuracy | 90.1 |
Domain Adaptation | Office-Caltech | JGSA[[Zhang, Li, and Ogunbona2017]] | http://arxiv.org/abs/1705.05498v1 | Average Accuracy | 90.0 |
Domain Adaptation | Office-Caltech | DDC[[Tzeng et al.2014]] | http://arxiv.org/abs/1412.3474v1 | Average Accuracy | 88.2 |
Domain Adaptation | Office-Caltech | SCA[[Ghifary et al.2016]] | http://arxiv.org/abs/1510.04373v2 | Average Accuracy | 85.9 |
Domain Adaptation | Office-Caltech | CORAL[[Sun, Feng, and Saenko2017]] | http://arxiv.org/abs/1612.01939v1 | Average Accuracy | 84.7 |
Domain Adaptation | S2RDA-49 | PGA | https://arxiv.org/abs/2406.09353v3 | Accuracy | 74.1 |
Domain Adaptation | Office-Caltech-10 | MEDA | http://arxiv.org/abs/1807.07258v2 | Accuracy (%) | 92.8 |
Domain Adaptation | VisDA2017 | FFTAT | https://arxiv.org/abs/2411.07794v1 | Accuracy | 93.8 |
Domain Adaptation | VisDA2017 | RCL | https://arxiv.org/abs/2405.18376v1 | Accuracy | 93.2 |
Domain Adaptation | VisDA2017 | MIC | https://arxiv.org/abs/2212.01322v2 | Accuracy | 92.8 |
Domain Adaptation | VisDA2017 | SWG | https://arxiv.org/abs/2312.04066v4 | Accuracy | 92.7 |
Domain Adaptation | VisDA2017 | CMKD | https://ieeexplore.ieee.org/document/10505301 | Accuracy | 91.8 |
Domain Adaptation | VisDA2017 | DePT | https://arxiv.org/abs/2210.04831v2 | Accuracy | 90.7 |
Domain Adaptation | VisDA2017 | SDAT(ViT) | https://arxiv.org/abs/2206.08213v1 | Accuracy | 89.8 |
Domain Adaptation | VisDA2017 | SFDA2++ | https://arxiv.org/abs/2403.10834v1 | Accuracy | 89.6 |
Domain Adaptation | VisDA2017 | PMtrans | https://arxiv.org/abs/2303.13434v2 | Accuracy | 88.8 |
Domain Adaptation | VisDA2017 | CoVi | https://arxiv.org/abs/2111.13353v3 | Accuracy | 88.5 |
Domain Adaptation | VisDA2017 | CDTrans | https://arxiv.org/abs/2109.06165v4 | Accuracy | 88.4 |
Domain Adaptation | VisDA2017 | SFDA2 | https://arxiv.org/abs/2403.10834v1 | Accuracy | 88.1 |
Domain Adaptation | VisDA2017 | FixBi | https://arxiv.org/abs/2011.09230v2 | Accuracy | 87.2 |
Domain Adaptation | VisDA2017 | CAN | http://arxiv.org/abs/1901.00976v2 | Accuracy | 87.2 |
Domain Adaptation | VisDA2017 | dSNE | http://openaccess.thecvf.com/content_CVPR_2019/html/Xu_d-SNE_Domain_Adaptation_Using_Stochastic_Neighborhood_Embedding_CVPR_2019_paper.html | Accuracy | 86.15 |
Domain Adaptation | VisDA2017 | CPGA | https://arxiv.org/abs/2106.15326v1 | Accuracy | 86.0 |
Domain Adaptation | VisDA2017 | Mean teacher | http://arxiv.org/abs/1706.05208v4 | Accuracy | 85.4 |
Domain Adaptation | VisDA2017 | MCC+NWD | https://arxiv.org/abs/2204.03838v1 | Accuracy | 83.7 |
Domain Adaptation | VisDA2017 | SHOT | https://arxiv.org/abs/2002.08546v6 | Accuracy | 82.9 |
Domain Adaptation | VisDA2017 | DTA | https://arxiv.org/abs/1910.05562v1 | Accuracy | 81.5 |
Domain Adaptation | VisDA2017 | MRKLD + LRENT | https://arxiv.org/abs/1908.09822v3 | Accuracy | 78.1 |
Domain Adaptation | VisDA2017 | CRST | https://arxiv.org/abs/1908.09822v3 | Accuracy | 78.1 |
Domain Adaptation | VisDA2017 | SENTRY (ResNet50) | https://arxiv.org/abs/2012.11460v2 | Accuracy | 76.7 |
Domain Adaptation | VisDA2017 | SWD | http://arxiv.org/abs/1903.04064v1 | Accuracy | 76.4 |
Domain Adaptation | VisDA2017 | IAFN | https://arxiv.org/abs/1811.07456v2 | Accuracy | 76.1 |
Domain Adaptation | VisDA2017 | CDAN | http://arxiv.org/abs/1705.10667v4 | Accuracy | 73.7 |
Domain Adaptation | VisDA2017 | DeepJDOT | http://arxiv.org/abs/1803.10081v3 | Accuracy | 66.9 |
Domain Adaptation | VisDA2017 | JAN | http://arxiv.org/abs/1605.06636v2 | Accuracy | 58.3 |
Domain Adaptation | MNIST-to-USPS | FACT | https://arxiv.org/abs/2306.00607v2 | Accuracy | 98.8 |
Domain Adaptation | MNIST-to-USPS | FAMCD | https://doi.org/10.1117/12.2646422 | Accuracy | 98.72 |
Domain Adaptation | MNIST-to-USPS | DFA-MCD | https://arxiv.org/abs/2006.12770v5 | Accuracy | 98.6 |
Domain Adaptation | MNIST-to-USPS | Mean teacher | http://arxiv.org/abs/1706.05208v4 | Accuracy | 98.26 |
Domain Adaptation | MNIST-to-USPS | DRANet | https://arxiv.org/abs/2103.13447v2 | Accuracy | 98.2 |
Domain Adaptation | MNIST-to-USPS | SHOT | https://arxiv.org/abs/2002.08546v6 | Accuracy | 98.0 |
Domain Adaptation | MNIST-to-USPS | DFA-ENT | https://arxiv.org/abs/2006.12770v5 | Accuracy | 97.9 |
Domain Adaptation | MNIST-to-USPS | CyCleGAN (Light-weight Calibrator) | https://arxiv.org/abs/1911.12796v2 | Accuracy | 97.1 |
Domain Adaptation | MNIST-to-USPS | 3CATN | https://arxiv.org/abs/1909.07618v1 | Accuracy | 96.1 |
Domain Adaptation | MNIST-to-USPS | rRevGrad+CAT | https://arxiv.org/abs/1903.09980v2 | Accuracy | 96 |
Domain Adaptation | MNIST-to-USPS | DeepJDOT | http://arxiv.org/abs/1803.10081v3 | Accuracy | 95.7 |
Domain Adaptation | MNIST-to-USPS | SRDA (RAN) | https://arxiv.org/abs/1905.10748v4 | Accuracy | 94.76 |
Domain Adaptation | MNIST-to-USPS | MCD | http://arxiv.org/abs/1712.02560v4 | Accuracy | 93.8 |
Domain Adaptation | MNIST-to-USPS | ADDN | http://arxiv.org/abs/1702.05464v1 | Accuracy | 90.1 |
Domain Adaptation | Noisy-Amazon (20%) | Butterfly | https://arxiv.org/abs/1905.07720v3 | Average Accuracy | 71.53 |
Domain Adaptation | HMDBsmall-to-UCF | TA3N | https://arxiv.org/abs/1905.10861v5 | Accuracy | 99.47 |
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