3 papers
cs.LG2019
Improving Reproducible Deep Learning Workflows with DeepDIVA
Michele Alberti, Vinaychandran Pondenkandath, Lars Vögtlin +3
The field of deep learning is experiencing a trend towards producing reproducible research. Nevertheless, it is still often a frustrating experience to reproduce scientific results…
cs.LG2018
Are You Tampering With My Data?
Michele Alberti, Vinaychandran Pondenkandath, Marcel Würsch +4
We propose a novel approach towards adversarial attacks on neural networks (NN), focusing on tampering the data used for training instead of generating attacks on trained models. O…
cs.CV2018
DeepDIVA: A Highly-Functional Python Framework for Reproducible Experiments
Michele Alberti, Vinaychandran Pondenkandath, Marcel Würsch +2
We introduce DeepDIVA: an infrastructure designed to enable quick and intuitive setup of reproducible experiments with a large range of useful analysis functionality. Reproducing s…