14 citations · 15 across the 3 of their papers we have counts for
3 papers
PriPHiT: Privacy-Preserving Hierarchical Training of Deep Neural Networks
Yamin Sepehri, Pedram Pad, Pascal Frossard +1
The training phase of deep neural networks requires substantial resources and as such is often performed on cloud servers. However, this raises privacy concerns when the training d…
Hierarchical Training of Deep Neural Networks Using Early Exiting
Yamin Sepehri, Pedram Pad, Ahmet Caner Yüzügüler +2
Deep neural networks provide state-of-the-art accuracy for vision tasks but they require significant resources for training. Thus, they are trained on cloud servers far from the ed…
Privacy-Preserving Image Acquisition Using Trainable Optical Kernel
Yamin Sepehri, Pedram Pad, Pascal Frossard +1
Preserving privacy is a growing concern in our society where sensors and cameras are ubiquitous. In this work, for the first time, we propose a trainable image acquisition method t…