{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T08:11:35Z","timestamp":1780474295986,"version":"3.54.1"},"reference-count":52,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2018,9,18]],"date-time":"2018-09-18T00:00:00Z","timestamp":1537228800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CNS 16-18627, CNS 13-20209"],"award-info":[{"award-number":["CNS 16-18627, CNS 13-20209"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006754","name":"Army Research Laboratory","doi-asserted-by":"publisher","award":["W911NF-09-2-0053,W911NF-17-2-0196"],"award-info":[{"award-number":["W911NF-09-2-0053,W911NF-17-2-0196"]}],"id":[{"id":"10.13039\/100006754","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2018,9,18]]},"abstract":"<jats:p>Recent proliferation of Internet of Things (IoT) devices with enhanced computing and sensing capabilities has revolutionized our everyday life. The massive data from these ubiquitous devices motivate the creation of intelligent IoT systems that can collectively learn. However, labelling data for learning purposes is extremely time-consuming, which greatly hinders deployment. In this paper, we describe a semi-supervised deep learning framework, called SenseGAN, that can leverage abundant unlabelled sensing data thereby minimizing the need for labelling effort. SenseGAN jointly trains three components with an adversarial game: (i) a classifier for predicting labels of input sensing data; (ii) a generator for generating sensing data samples based on the input labels; and (iii) a discriminator for differentiating the joint data\/label distribution between real samples and partially generated samples from either the classifier or the generator. The classifier and the generator try to generate fake data\/labels that can fool the discriminator. The adversarial game among the three components can mutually boost their performance, which helps the classifier learn to predict correct labels with unlabelled data in return. SenseGAN can effectively handle multimodal sensing inputs and easily stabilize the adversarial training process, which helps improve the performance of the classifier. Experiments on three IoT applications demonstrate the substantial improvements in accuracy and F1 score under SenseGAN, compared with supervised counterparts trained only on the labelled portion of the data, as well as other supervised and semi-supervised baselines. For these three applications, SenseGAN requires only 10% of the originally labelled data, to attain nearly the same accuracy as a deep learning classifier trained on the fully labelled dataset.<\/jats:p>","DOI":"10.1145\/3264954","type":"journal-article","created":{"date-parts":[[2018,9,19]],"date-time":"2018-09-19T11:58:41Z","timestamp":1537358321000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":44,"title":["SenseGAN"],"prefix":"10.1145","volume":"2","author":[{"given":"Shuochao","family":"Yao","sequence":"first","affiliation":[{"name":"University of Illinois Urbana Champaign"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiran","family":"Zhao","sequence":"additional","affiliation":[{"name":"University of Illinois Urbana Champaign"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huajie","family":"Shao","sequence":"additional","affiliation":[{"name":"University of Illinois Urbana Champaign"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Illinois Urbana Champaign"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aston","family":"Zhang","sequence":"additional","affiliation":[{"name":"Amazon AI"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaohan","family":"Hu","sequence":"additional","affiliation":[{"name":"IBM Research"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongxin","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Illinois Urbana Champaign"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shengzhong","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Illinois Urbana Champaign"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu","family":"Su","sequence":"additional","affiliation":[{"name":"State University of New York at Buffalo"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tarek","family":"Abdelzaher","sequence":"additional","affiliation":[{"name":"University of Illinois Urbana Champaign"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2018,9,18]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Intel edison compute module. http:\/\/www.intel.com\/content\/dam\/support\/us\/en\/documents\/edison\/sb\/edison-module_HG_331189.pdf.  Intel edison compute module. http:\/\/www.intel.com\/content\/dam\/support\/us\/en\/documents\/edison\/sb\/edison-module_HG_331189.pdf."},{"key":"e_1_2_1_2_1","volume-title":"Wasserstein gan. arXiv preprint arXiv:1701.07875","author":"Arjovsky M.","year":"2017","unstructured":"M. Arjovsky , S. Chintala , and L. Bottou . Wasserstein gan. arXiv preprint arXiv:1701.07875 , 2017 . M. Arjovsky, S. Chintala, and L. Bottou. Wasserstein gan. arXiv preprint arXiv:1701.07875, 2017."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2207676.2208600"},{"key":"e_1_2_1_4_1","first-page":"368","volume-title":"Advances in Neural Information processing systems","author":"Bennett K. P.","year":"1999","unstructured":"K. P. Bennett and A. Demiriz . Semi-supervised support vector machines . In Advances in Neural Information processing systems , pages 368 -- 374 , 1999 . K. P. Bennett and A. Demiriz. Semi-supervised support vector machines. In Advances in Neural Information processing systems, pages 368--374, 1999."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/2702123.2702208"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2632048.2632090"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/MPRV.2008.39"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/1999995.2000010"},{"key":"e_1_2_1_9_1","volume-title":"ICML","author":"Dauphin Y.","year":"2017","unstructured":"Y. Dauphin , A. Fan , M. Auli , and D. Grangier . Language modeling with gated convolutional networks . In ICML , 2017 . Y. Dauphin, A. Fan, M. Auli, and D. Grangier. Language modeling with gated convolutional networks. In ICML, 2017."},{"key":"e_1_2_1_10_1","volume-title":"Semi-supervised learning with context-conditional generative adversarial networks. arXiv preprint arXiv:1611.06430","author":"Denton E.","year":"2016","unstructured":"E. Denton , S. Gross , and R. Fergus . Semi-supervised learning with context-conditional generative adversarial networks. arXiv preprint arXiv:1611.06430 , 2016 . E. Denton, S. Gross, and R. Fergus. Semi-supervised learning with context-conditional generative adversarial networks. arXiv preprint arXiv:1611.06430, 2016."},{"key":"e_1_2_1_11_1","volume-title":"Adversarially learned inference. arXiv preprint arXiv:1606.00704","author":"Dumoulin V.","year":"2016","unstructured":"V. Dumoulin , I. Belghazi , B. Poole , A. Lamb , M. Arjovsky , O. Mastropietro , and A. Courville . Adversarially learned inference. arXiv preprint arXiv:1606.00704 , 2016 . V. Dumoulin, I. Belghazi, B. Poole, A. Lamb, M. Arjovsky, O. Mastropietro, and A. Courville. Adversarially learned inference. arXiv preprint arXiv:1606.00704, 2016."},{"key":"e_1_2_1_12_1","volume-title":"International journal of methods in psychiatric research, 25(4):309--323","author":"Faurholt-Jepsen M.","year":"2016","unstructured":"M. Faurholt-Jepsen , M. Vinberg , M. Frost , S. Debel , E. Margrethe Christensen , J. E. Bardram , and L. V. Kessing . Behavioral activities collected through smartphones and the association with illness activity in bipolar disorder . International journal of methods in psychiatric research, 25(4):309--323 , 2016 . M. Faurholt-Jepsen, M. Vinberg, M. Frost, S. Debel, E. Margrethe Christensen, J. E. Bardram, and L. V. Kessing. Behavioral activities collected through smartphones and the association with illness activity in bipolar disorder. International journal of methods in psychiatric research, 25(4):309--323, 2016."},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00779-010-0293-9"},{"key":"e_1_2_1_14_1","first-page":"2672","volume-title":"Advances in neural information processing systems","author":"Goodfellow I.","year":"2014","unstructured":"I. Goodfellow , J. Pouget-Abadie , M. Mirza , B. Xu , D. Warde-Farley , S. Ozair , A. Courville , and Y. Bengio . Generative adversarial nets . In Advances in neural information processing systems , pages 2672 -- 2680 , 2014 . I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. Generative adversarial nets. In Advances in neural information processing systems, pages 2672--2680, 2014."},{"key":"e_1_2_1_15_1","volume-title":"Variance reduction techniques for gradient estimates in reinforcement learning. Journal of Machine Learning Research, 5(Nov):1471--1530","author":"Greensmith E.","year":"2004","unstructured":"E. Greensmith , P. L. Bartlett , and J. Baxter . Variance reduction techniques for gradient estimates in reinforcement learning. Journal of Machine Learning Research, 5(Nov):1471--1530 , 2004 . E. Greensmith, P. L. Bartlett, and J. Baxter. Variance reduction techniques for gradient estimates in reinforcement learning. Journal of Machine Learning Research, 5(Nov):1471--1530, 2004."},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/2632048.2632099"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/2906388.2906402"},{"key":"e_1_2_1_18_1","volume-title":"Improved training of wasserstein gans. arXiv preprint arXiv:1704.00028","author":"Gulrajani I.","year":"2017","unstructured":"I. Gulrajani , F. Ahmed , M. Arjovsky , V. Dumoulin , and A. Courville . Improved training of wasserstein gans. arXiv preprint arXiv:1704.00028 , 2017 . I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. Courville. Improved training of wasserstein gans. arXiv preprint arXiv:1704.00028, 2017."},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/2493988.2494353"},{"key":"e_1_2_1_20_1","volume-title":"Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv preprint arXiv:1510.00149","author":"Han S.","year":"2015","unstructured":"S. Han , H. Mao , and W. J. Dally . Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv preprint arXiv:1510.00149 , 2015 . S. Han, H. Mao, and W. J. Dally. Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv preprint arXiv:1510.00149, 2015."},{"key":"e_1_2_1_21_1","volume-title":"Wisture: Rnn-based learning of wireless signals for gesture recognition in unmodified smartphones. arXiv preprint arXiv:1707.08569","author":"Haseeb M. A. A.","year":"2017","unstructured":"M. A. A. Haseeb and R. Parasuraman . Wisture: Rnn-based learning of wireless signals for gesture recognition in unmodified smartphones. arXiv preprint arXiv:1707.08569 , 2017 . M. A. A. Haseeb and R. Parasuraman. Wisture: Rnn-based learning of wireless signals for gesture recognition in unmodified smartphones. arXiv preprint arXiv:1707.08569, 2017."},{"key":"e_1_2_1_22_1","volume-title":"Categorical reparameterization with gumbel-softmax. arXiv preprint arXiv:1611.01144","author":"Jang E.","year":"2016","unstructured":"E. Jang , S. Gu , and B. Poole . Categorical reparameterization with gumbel-softmax. arXiv preprint arXiv:1611.01144 , 2016 . E. Jang, S. Gu, and B. Poole. Categorical reparameterization with gumbel-softmax. arXiv preprint arXiv:1611.01144, 2016."},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/2370216.2370282"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/2971648.2971726"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/2632048.2632069"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/MPRV.2017.2940968"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/2750858.2804262"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"e_1_2_1_29_1","volume-title":"Triple generative adversarial nets. arXiv preprint arXiv:1703.02291","author":"Li C.","year":"2017","unstructured":"C. Li , K. Xu , J. Zhu , and B. Zhang . Triple generative adversarial nets. arXiv preprint arXiv:1703.02291 , 2017 . C. Li, K. Xu, J. Zhu, and B. Zhang. Triple generative adversarial nets. arXiv preprint arXiv:1703.02291, 2017."},{"key":"e_1_2_1_30_1","volume-title":"Activity recognition using a single accelerometer placed at the wrist or ankle. Medicine and science in sports and exercise, 45(11):2193","author":"Mannini A.","year":"2013","unstructured":"A. Mannini , S. S. Intille , M. Rosenberger , A. M. Sabatini , and W. Haskell . Activity recognition using a single accelerometer placed at the wrist or ankle. Medicine and science in sports and exercise, 45(11):2193 , 2013 . A. Mannini, S. S. Intille, M. Rosenberger, A. M. Sabatini, and W. Haskell. Activity recognition using a single accelerometer placed at the wrist or ankle. Medicine and science in sports and exercise, 45(11):2193, 2013."},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/2632048.2632095"},{"key":"e_1_2_1_32_1","volume-title":"Semi-supervised learning with generative adversarial networks. arXiv preprint arXiv:1606.01583","author":"Odena A.","year":"2016","unstructured":"A. Odena . Semi-supervised learning with generative adversarial networks. arXiv preprint arXiv:1606.01583 , 2016 . A. Odena. Semi-supervised learning with generative adversarial networks. arXiv preprint arXiv:1606.01583, 2016."},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/2500423.2500436"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/2968219.2971461"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/2809695.2809718"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/2207676.2208603"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-013-0706-y"},{"key":"e_1_2_1_38_1","volume-title":"old and new","author":"Villani C.","year":"2008","unstructured":"C. Villani . Optimal transport : old and new , volume 338 . Springer Science 8 Business Media, 2008 . C. Villani. Optimal transport: old and new, volume 338. Springer Science 8 Business Media, 2008."},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/2971648.2971653"},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/2968219.2968414"},{"key":"e_1_2_1_41_1","first-page":"2048","volume-title":"International Conference on Machine Learning","author":"Xu K.","year":"2015","unstructured":"K. Xu , J. Ba , R. Kiros , K. Cho , A. Courville , R. Salakhudinov , R. Zemel , and Y. Bengio . Show, attend and tell: Neural image caption generation with visual attention . In International Conference on Machine Learning , pages 2048 -- 2057 , 2015 . K. Xu, J. Ba, R. Kiros, K. Cho, A. Courville, R. Salakhudinov, R. Zemel, and Y. Bengio. Show, attend and tell: Neural image caption generation with visual attention. In International Conference on Machine Learning, pages 2048--2057, 2015."},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/2971648.2971701"},{"key":"e_1_2_1_43_1","first-page":"14","volume-title":"Proceedings of the 15th International Conference on Information Processing in Sensor Networks","author":"Yao S.","unstructured":"S. Yao , M. T. Amin , L. Su , S. Hu , S. Li , S. Wang , Y. Zhao , T. Abdelzaher , L. Kaplan , C. Aggarwal , Recursive ground truth estimator for social data streams . In Proceedings of the 15th International Conference on Information Processing in Sensor Networks , page 14 . IEEE Press, 2016. S. Yao, M. T. Amin, L. Su, S. Hu, S. Li, S. Wang, Y. Zhao, T. Abdelzaher, L. Kaplan, C. Aggarwal, et al. Recursive ground truth estimator for social data streams. In Proceedings of the 15th International Conference on Information Processing in Sensor Networks, page 14. IEEE Press, 2016."},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS.2016.75"},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052577"},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3161181"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2018.2381131"},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3131672.3131675"},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/2935334.2935375"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098027"},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052601"},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00779-015-0866-8"}],"container-title":["Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3264954","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3264954","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3264954","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T02:08:00Z","timestamp":1750212480000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3264954"}},"subtitle":["Enabling Deep Learning for Internet of Things with a Semi-Supervised Framework"],"short-title":[],"issued":{"date-parts":[[2018,9,18]]},"references-count":52,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2018,9,18]]}},"alternative-id":["10.1145\/3264954"],"URL":"https:\/\/doi.org\/10.1145\/3264954","relation":{},"ISSN":["2474-9567"],"issn-type":[{"value":"2474-9567","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,9,18]]},"assertion":[{"value":"2018-02-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2018-09-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2018-09-18","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}