2k citations · 2.8k across the 2 of their papers we have counts for
3 papers
Unsupervised Deep Feature Extraction for Remote Sensing Image Classification
Adriana Romero, Carlo Gatta, Gustau Camps-Valls
This paper introduces the use of single layer and deep convolutional networks for remote sensing data analysis. Direct application to multi- and hyper-spectral imagery of supervise…
FitNets: Hints for Thin Deep Nets
Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou +3
While depth tends to improve network performances, it also makes gradient-based training more difficult since deeper networks tend to be more non-linear. The recently proposed know…
No more meta-parameter tuning in unsupervised sparse feature learning
Adriana Romero, Petia Radeva, Carlo Gatta
We propose a meta-parameter free, off-the-shelf, simple and fast unsupervised feature learning algorithm, which exploits a new way of optimizing for sparsity. Experiments on STL-10…