Abstract
We present a novel generative adversarial network (GAN) model, called InsightGAN, for drug abuse detection. Our model is inspired by two closely related works on machine learning for healthcare applications: (1) drug abuse detection has been solved by machine learning with plentiful data from social media (where face pictures can be easily obtained); (2) facial characteristics have been explored in mental disorder diagnosis (drug addiction is also a mental disorder). In this paper, we adopt deep learning to extract discriminative facial features for drug abuse detection. However, in this application, the face pictures with ground-truth labels are far from sufficient for training a deep learning model. To alleviate the scarcity of labelled data, we thus propose a semi-supervised facial feature learning model based on GAN. Moreover, we also develop a robust algorithm for training our InsightGAN. Experimental results show the promising performance of our InsightGAN.
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Acknowledgements
This work was partially supported by National Natural Science Foundation of China (61573363), and the Fundamental Research Funds for the Central Universities and the Research Funds of Renmin University of China (15XNLQ01).
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Liu, G., Hu, J., Zhao, A., Ding, M., Huo, Y., Lu, Z. (2018). InsightGAN: Semi-Supervised Feature Learning with Generative Adversarial Network for Drug Abuse Detection. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11303. Springer, Cham. https://doi.org/10.1007/978-3-030-04182-3_36
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