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Cited by 36Context-Aware Activity Recognition SystemsDomain Adaptation and Few-Shot LearningGait Recognition and Analysis

The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

Semi-supervised learning for human activity recognition using adversarial autoencoders

Balabka, Dmitrijs·2019·Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers
DOI 10.1145/3341162.3344854
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Abstract

SHL recognition challenge 2019 goal is to recognize eight locomotion and transportation (activities) from the inertial sensor data of a smartphone. The dataset contains information from different mobile-phones placement (torso, bag, hips, hand). Participants must provide their predictions based on test data that contains Hand phone sensors information. Only a small amount of Hand phone labeled data exists in the validation data. Train data consists only of torso, bag and hips placed mobile devices. Team DB proposes to apply deep semi-supervised learning. As the base for our model, we have chosen Adversarial Autoencoder (AAE) and employ Convolutional Networks for feature extraction. We prove that semi-supervised learning gives possibility to utilize test unlabeled data during AAE training with small amount of validation labeled data and achieve high model accuracy for Human Activity Recognition task.

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Research works printed but not linked: 9

  1. Elie Aljalbout et al. 2018 . Clustering with Deep Learning: Taxonomy and New Methods. arXiv Prepr. arXiv1801.07648 (2018). Retrieved from http://arxiv.org/abs/1801.07648 Elie Aljalbout et al. 2018. Clustering with Deep Learning: Taxonomy and New Methods. arXiv Prepr. arXiv1801.07648 (2018). Retrieved from http://arxiv.org/abs/1801.07648 · arXiv
  2. Martin Arjovsky et al. 2017 . Wasserstein Generative Adversarial Network. In International Conference on Machine Learning, 1--10 . Martin Arjovsky et al. 2017. Wasserstein Generative Adversarial Network. In International Conference on Machine Learning, 1--10.
  3. Soumith Chantala . 2016 . How to Train a GAN . In NIPS 2016 Workshop on Adversarial Training. Soumith Chantala. 2016. How to Train a GAN. In NIPS 2016 Workshop on Adversarial Training.
  4. Ian Goodfellow et al. 2014 . Generative Adversarial Nets. Adv. Neural Inf. Process. Syst. 27 (2014), 2672 -- 2680 . Ian Goodfellow et al. 2014. Generative Adversarial Nets. Adv. Neural Inf. Process. Syst. 27 (2014), 2672--2680.
  5. Sergey Ioffe . 2017 . Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models. Adv. Neural Inf. Process. Syst. (2017) . Retrieved November 11, 2018 from http://arxiv.org/abs/1702.03275 Sergey Ioffe. 2017. Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models. Adv. Neural Inf. Process. Syst. (2017). Retrieved November 11, 2018 from http://arxiv.org/abs/1702.03275 · arXiv
  6. Diederik P. Kingma and Jimmy Ba . 2014 . Adam : A Method for Stochastic Optimization. arXiv Prepr. arXiv1412.6980 (2014). Retrieved from http://arxiv.org/abs/1412.6980 Diederik P. Kingma and Jimmy Ba. 2014. Adam: A Method for Stochastic Optimization. arXiv Prepr. arXiv1412.6980 (2014). Retrieved from http://arxiv.org/abs/1412.6980 · arXiv
  7. Xiang Li et al. 2018. Understanding the Disharmony between Dropout and Batch Normalization by Variance Shift. arXiv Prepr. arXiv1801.05134 (2018) . Retrieved May 21, 2019 from http://arxiv.org/abs/1801.05134 Xiang Li et al. 2018. Understanding the Disharmony between Dropout and Batch Normalization by Variance Shift. arXiv Prepr. arXiv1801.05134 (2018). Retrieved May 21, 2019 from http://arxiv.org/abs/1801.05134 · arXiv
  8. Alireza Makhzani et al. 2015. Adversarial Autoencoders. arXiv Prepr. arXiv1511.05644 (2015). Retrieved from http://arxiv.org/abs/1511.05644 Alireza Makhzani et al. 2015. Adversarial Autoencoders. arXiv Prepr. arXiv1511.05644 (2015). Retrieved from http://arxiv.org/abs/1511.05644 · arXiv
  9. Sudhanshu Mittal . 2017. Semi-supervised Learning for Real-world Object Recognition using Adversarial Autoencoders. Master's thesis . Royal Institute of Technology (KTH) , Stockholm, Sweden . Retrieved May 23, 2018 from http://www.nada.kth.se/~ann/exjobb/sudhanshu_mittal.pdf Sudhanshu Mittal. 2017. Semi-supervised Learning for Real-world Object Recognition using Adversarial Autoencoders. Master's thesis. Royal Institute of Technology (KTH), Stockholm, Sweden. Retrieved May 23, 2018 from http://www.nada.kth.se/~ann/exjobb/sudhanshu_mittal.pdf

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Metadata

Authors
Balabka, D.
Affiliations
Transport and Telecommunication Institute (LV)
Corresponding author
Balabka, Dmitrijs
Venue
Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers
Publisher
ACM
Published
2019-09-09
Pages
685-688
DOI
10.1145/3341162.3344854
OpenAlex
W2972376155
Cited by
36
References
9
Citations by year
2025: 2 · 2024: 9 · 2023: 7 · 2022: 9 · 2021: 5 · 2020: 3 · 2019: 1
FWCI
1.47
Type
conference paper
Language
en
Open access
closed
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
  • Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
Keywords
human activity recognition, semi-supervised learning, adversarial autoencoder, inertial data, smartphone sensors, unlabeled test data, convolutional neural networks

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