Enabling Privacy with Transfer Learning for Image Classification DNNs on Mobile Devices

Abstract

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. CCS CONCEPTS • Human-centered computing → Mobile devices; • Computing methodologies → Transfer learning;

Mobile Sensing for Wellbeing Estimation of Urban Green using Physiological Signals

“…Fine grain assessment on environmental and personal factors combined so far have not been used together with machine learning to gain models that can be personalized. Nonetheless, adaption of machine learning models on mobile device is a feasible task [19]. While deep learning is becoming increasingly popular in Affective Computing [24] we rely upon handcrafted features due to the sparse nature of our data-set [23].…”