SWL-Adapt: An Unsupervised Domain Adaptation Model with Sample Weight Learning for Cross-User Wearable Human Activity Recognition
暂无分享,去创建一个
[1] K. Mao,et al. Calibrating Class Weights with Multi-Modal Information for Partial Video Domain Adaptation , 2022, ACM Multimedia.
[2] H. Bischof,et al. The Norm Must Go On: Dynamic Unsupervised Domain Adaptation by Normalization , 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[3] Hui Xue,et al. Adversarial Spectral Kernel Matching for Unsupervised Time Series Domain Adaptation , 2021, IJCAI.
[4] Lior Wolf,et al. Sample Selection for Universal Domain Adaptation , 2021, AAAI.
[5] Daniela Micucci,et al. Personalized Models in Human Activity Recognition using Deep Learning , 2021, 2020 25th International Conference on Pattern Recognition (ICPR).
[6] Avijoy Chakma,et al. Activity recognition in wearables using adversarial multi-source domain adaptation , 2020 .
[7] Rubén San-Segundo,et al. Adaptation and Selection techniques based on Deep Learning for Human Activity Recognition using Inertial Sensors , 2020, Proceedings of 7th International Electronic Conference on Sensors and Applications.
[8] Sozo Inoue,et al. Improving activity data collection with on-device personalization using fine-tuning , 2020, UbiComp/ISWC Adjunct.
[9] Jing Zhao,et al. XHAR: Deep Domain Adaptation for Human Activity Recognition with Smart Devices , 2020, 2020 17th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON).
[10] Diane J. Cook,et al. Multi-Source Deep Domain Adaptation with Weak Supervision for Time-Series Sensor Data , 2020, KDD.
[11] T. Abdelzaher,et al. GIobalFusion: A Global Attentional Deep Learning Framework for Multisensor Information Fusion , 2020, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..
[12] Junehwa Song,et al. A Systematic Study of Unsupervised Domain Adaptation for Robust Human-Activity Recognition , 2020, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..
[13] Ling Chen,et al. METIER: A Deep Multi-Task Learning Based Activity and User Recognition Model Using Wearable Sensors , 2020, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..
[14] Natalia Gimelshein,et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.
[15] Artem Molchanov,et al. Generalized Inner Loop Meta-Learning , 2019, ArXiv.
[16] Jianmin Wang,et al. Transferable Curriculum for Weakly-Supervised Domain Adaptation , 2019, AAAI.
[17] Han Zou,et al. Consensus Adversarial Domain Adaptation , 2019, AAAI.
[18] Majid Sarrafzadeh,et al. Domain Adaptation in Children Activity Recognition , 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[19] Michael I. Jordan,et al. Universal Domain Adaptation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[20] Michael I. Jordan,et al. Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation , 2019, ICML.
[21] Sergiu M. Dascalu,et al. Spatiotemporal recursive hyperspheric classification with an application to dynamic gesture recognition , 2019, Artif. Intell..
[22] Elnaz Soleimani,et al. Cross-Subject Transfer Learning in Human Activity Recognition Systems using Generative Adversarial Networks , 2019, Neurocomputing.
[23] Jianmin Wang,et al. Learning to Transfer Examples for Partial Domain Adaptation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[24] Dianhui Chu,et al. Empirical Study and Improvement on Deep Transfer Learning for Human Activity Recognition , 2018, Sensors.
[25] Lina Yao,et al. Distributionally Robust Semi-Supervised Learning for People-Centric Sensing , 2018, AAAI.
[26] Jianmin Wang,et al. Partial Adversarial Domain Adaptation , 2018, ECCV.
[27] Jing Zhang,et al. Importance Weighted Adversarial Nets for Partial Domain Adaptation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[28] Archan Misra,et al. Scaling Human Activity Recognition via Deep Learning-based Domain Adaptation , 2018, 2018 IEEE International Conference on Pervasive Computing and Communications (PerCom).
[29] Hassan Ghasemzadeh,et al. Personalized Human Activity Recognition Using Convolutional Neural Networks , 2018, AAAI.
[30] Tatsuya Harada,et al. Maximum Classifier Discrepancy for Unsupervised Domain Adaptation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[31] Koichi Shinoda,et al. User adaptation of convolutional neural network for human activity recognition , 2017, 2017 25th European Signal Processing Conference (EUSIPCO).
[32] Sergey Levine,et al. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks , 2017, ICML.
[33] Trevor Darrell,et al. Adversarial Discriminative Domain Adaptation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[34] Michael I. Jordan,et al. Deep Transfer Learning with Joint Adaptation Networks , 2016, ICML.
[35] Pascal Fua,et al. Beyond Sharing Weights for Deep Domain Adaptation , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[36] Timo Sztyler,et al. On-body localization of wearable devices: An investigation of position-aware activity recognition , 2016, 2016 IEEE International Conference on Pervasive Computing and Communications (PerCom).
[37] Jin-Hyuk Hong,et al. Toward Personalized Activity Recognition Systems With a Semipopulation Approach , 2016, IEEE Transactions on Human-Machine Systems.
[38] François Laviolette,et al. Domain-Adversarial Training of Neural Networks , 2015, J. Mach. Learn. Res..
[39] Michael I. Jordan,et al. Learning Transferable Features with Deep Adaptation Networks , 2015, ICML.
[40] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[41] Victor S. Lempitsky,et al. Unsupervised Domain Adaptation by Backpropagation , 2014, ICML.
[42] Paul Lukowicz,et al. Collecting complex activity datasets in highly rich networked sensor environments , 2010, 2010 Seventh International Conference on Networked Sensing Systems (INSS).
[43] Koby Crammer,et al. A theory of learning from different domains , 2010, Machine Learning.
[44] Ig-Jae Kim,et al. Mobile health monitoring system based on activity recognition using accelerometer , 2010, Simul. Model. Pract. Theory.
[45] Davide Anguita,et al. Transition-Aware Human Activity Recognition Using Smartphones , 2016, Neurocomputing.
[46] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .