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AdaSpring: Context-adaptive and Runtime-evolutionary Deep Model Compression for Mobile Applications
There are many deep learning (e. g., DNN) powered mobile and wearable applications today continuously and unobtrusively sensing the ambient surroundings to enhance all aspects of human lives.
Privacy Adversarial Network: Representation Learning for Mobile Data Privacy
This work departs from prior works in methodology: we leverage adversarial learning to a better balance between privacy and utility.
AdaDeep: A Usage-Driven, Automated Deep Model Compression Framework for Enabling Ubiquitous Intelligent Mobiles
Recent breakthroughs in Deep Neural Networks (DNNs) have fueled a tremendously growing demand for bringing DNN-powered intelligence into mobile platforms.
Better accuracy with quantified privacy: representations learned via reconstructive adversarial network
This work represents a methodical departure from prior works: we balance between a measure of privacy and another of utility by leveraging adversarial learning to find a sweeter tradeoff.
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