14 citations · 19 across the 6 of their papers we have counts for
5 papers · 1 filter
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…
Adaptation of MobileNetV2 for Face Detection on Ultra-Low Power Platform
Simon Narduzzi, Engin Türetken, Jean-Philippe Thiran +1
Designing Deep Neural Networks (DNNs) running on edge hardware remains a challenge. Standard designs have been adopted by the community to facilitate the deployment of Neural Netwo…
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…
Leveraging Spatial and Photometric Context for Calibrated Non-Lambertian Photometric Stereo
David Honzátko, Engin Türetken, Pascal Fua +1
The problem of estimating a surface shape from its observed reflectance properties still remains a challenging task in computer vision. The presence of global illumination effects…