Constraining deep representations with a noise module for fair classification
Mattia Cerrato, Roberto Esposito, Laura Li Puma · 2020
The recent surge in interest for Deep Learning (motivated by its exceptional performances on longstanding problems) made Neural Networks a very appealing tool for many actors in our society. One issue in this shift of interest is that Neural Networks are very opaque objects and it is often hard to make sense of their predictions.