Site de Vincent Gripon

An Inside Look at Deep Neural Networks using Graph Signal Processing

Deep Neural Networks (DNNs) are state-of-the-art in many machine learning benchmarks. Understanding how they perform is a major open question. In this paper, we are interested in using graph signal processing to monitor the intermediate representations obtained in a simple DNN architecture. We compare different metrics and measures and show that smoothness of label signals on k-nearest neighbor graphs are a good candidate to interpret individual layers role in achieving good performance.

@inproceedings{GriOrtGir20182,
  author = {Vincent Gripon and Antonio Ortega and
Benjamin Girault},
  title = {An Inside Look at Deep Neural Networks
using Graph Signal Processing},
  booktitle = {Proceedings of ITA},
  year = {2018},
  pages = {1--9},
  month = {February},
}