Enabling Privacy with Transfer Learning for Image Classification DNNs on Mobile Devices
Andreas Seiderer, Michael Dietz, Ilhan Aslan, Elisabeth André · 2018
More people could benefit of Machine Learning (ML) as an increasingly important technology and service, if state-of-the-art ML techniques with training capability were accessible on personal devices. To this end, we report details on how to deploy Tensor-Flow on off-the-shelf mobile and embedded devices and retrain current deep neural networks for image recognition on-device. Our motivation is to both grant privacy and allow users to efficiently personalize image classifiers for their own needs and purposes, and thus contribute towards turning ML into a "social good", which benefits the largest number of people in the greatest possible way.