How Much Unlabeled Data is Really Needed for Effective Self-Supervised Human Activity Recognition?

Author:

Dhekane Sourish Gunesh1ORCID,Haresamudram Harish1ORCID,Thukral Megha1ORCID,Plötz Thomas1ORCID

Affiliation:

1. Georgia Institute of Technology, United States

Funder

National Science Foundation

Publisher

ACM

Reference39 articles.

1. An effective deep autoencoder approach for online smartphone-based human activity recognition;Almaslukh Bandar;Int. J. Comput. Sci. Netw. Secur,2017

2. Yuki  M Asano , Christian Rupprecht , and Andrea Vedaldi . 2019. A critical analysis of self-supervision, or what we can learn from a single image. arXiv preprint arXiv:1904.13132 ( 2019 ). Yuki M Asano, Christian Rupprecht, and Andrea Vedaldi. 2019. A critical analysis of self-supervision, or what we can learn from a single image. arXiv preprint arXiv:1904.13132 (2019).

3. Nicolas Carion , Francisco Massa , Gabriel Synnaeve , Nicolas Usunier , Alexander Kirillov , and Sergey Zagoruyko . 2020 . End-to-end object detection with transformers. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020 , Proceedings, Part I 16 . Springer, 213–229. Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. 2020. End-to-end object detection with transformers. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part I 16. Springer, 213–229.

4. Ting Chen , Simon Kornblith , Mohammad Norouzi , and Geoffrey Hinton . 2020 . A simple framework for contrastive learning of visual representations . In International conference on machine learning. PMLR, 1597–1607 . Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In International conference on machine learning. PMLR, 1597–1607.

5. Exploring Simple Siamese Representation Learning

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