Search Results for author: Christian Scharfenberger

Found 6 papers, 1 papers with code

Residual Error: a New Performance Measure for Adversarial Robustness

no code implementations18 Jun 2021 Hossein Aboutalebi, Mohammad Javad Shafiee, Michelle Karg, Christian Scharfenberger, Alexander Wong

Motivated by this, this study presents the concept of residual error, a new performance measure for not only assessing the adversarial robustness of a deep neural network at the individual sample level, but also can be used to differentiate between adversarial and non-adversarial examples to facilitate for adversarial example detection.

Adversarial Robustness Image Classification

Vulnerability Under Adversarial Machine Learning: Bias or Variance?

no code implementations1 Aug 2020 Hossein Aboutalebi, Mohammad Javad Shafiee, Michelle Karg, Christian Scharfenberger, Alexander Wong

In this study, we investigate the effect of adversarial machine learning on the bias and variance of a trained deep neural network and analyze how adversarial perturbations can affect the generalization of a network.

BIG-bench Machine Learning

StressedNets: Efficient Feature Representations via Stress-induced Evolutionary Synthesis of Deep Neural Networks

no code implementations16 Jan 2018 Mohammad Javad Shafiee, Brendan Chwyl, Francis Li, Rongyan Chen, Michelle Karg, Christian Scharfenberger, Alexander Wong

The computational complexity of leveraging deep neural networks for extracting deep feature representations is a significant barrier to its widespread adoption, particularly for use in embedded devices.

object-detection Object Detection

Non-contact transmittance photoplethysmographic imaging (PPGI) for long-distance cardiovascular monitoring

no code implementations23 Mar 2015 Robert Amelard, Christian Scharfenberger, Farnoud Kazemzadeh, Kaylen J. Pfisterer, Bill S. Lin, Alexander Wong, David A. Clausi

The results support the hypothesis that long-distance heart rate monitoring is feasible using transmittance PPGI, allowing for new possibilities of monitoring cardiovascular function in a non-contact manner.

Photoplethysmography (PPG)

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