@inproceedings{0481b6c23f494b31b25550287391ee9f,
title = "FeatureNET: Diversity-Driven Generation of Deep Learning Models",
abstract = "We present FeatureNET, an open-source Neural Architecture Search (NAS) tool 1 that generates diverse sets of Deep Learning (DL) models. FeatureNET relies on a meta-model of deep neural networks, consisting of generic configurable entities. Then, it uses tools developed in the context of software product lines to generate diverse (maximize the differences between the generated) DL models. The models are translated to Keras and can be integrated into typical machine learning pipelines. FeatureNET allows researchers to generate seamlessly a large variety of models. Thereby, it helps choosing appropriate DL models and performing experiments with diverse models (mitigating potential threats to validity). As a NAS method, FeatureNET successfully generates models performing equally well with handcrafted models.",
keywords = "AutoML, Configuration search, NAS, Neural Architecture Search",
author = "Salah Ghamizi and Maxime Cordy and Mike Papadakis and {Le Traon}, Yves",
note = "Publisher Copyright: {\textcopyright} 2020 ACM.; 42nd ACM/IEEE International Conference on Software Engineering: Companion, ICSE-Companion 2020 ; Conference date: 27-06-2020 Through 19-07-2020",
year = "2020",
month = oct,
doi = "10.1145/3377812.3382153",
language = "English",
series = "Proceedings - 2020 ACM/IEEE 42nd International Conference on Software Engineering: Companion, ICSE-Companion 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "41--44",
booktitle = "Proceedings - 2020 ACM/IEEE 42nd International Conference on Software Engineering",
address = "United States",
}