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Title:

Measuring Robustness in Deep Learning Based Compressive Sensing

Document type:
Konferenzbeitrag
Author(s):
Darestani, Mohammad Zalbagi; Chaudhari, Akshay S; Heckel, Reinhard
Abstract:
Deep neural networks give state-of-the-art accuracy for reconstructing images from few and noisy measurements, a problem arising for example in accelerated magnetic resonance imaging (MRI). However, recent works have raised concerns that deep-learning-based image reconstruction methods are sensitive to perturbations and are less robust than traditional methods: Neural networks (i) may be sensitive to small, yet adversarially-selected perturbations, (ii) may perform poorly under distribution shif...     »
Editor:
Meila, Marina; Zhang, Tong
Book / Congress title:
Proceedings of the 38th International Conference on Machine Learning
Volume:
139
Publisher:
PMLR
Year:
2021
Month:
18--24 Jul
Pages:
2433--2444
Bookseries title:
Proceedings of Machine Learning Research
WWW:
https://proceedings.mlr.press/v139/darestani21a.html
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