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IRIS è l’archivio della produzione scientifica della Fondazione Bruno Kessler.
EnglishDesigning neural networks for object recognition requires considerable architecture engineering. As a remedy, neuro-evolutionary network architecture search, which automatically searches for optimal network architectures using evolutionary algorithms, has recently become very popular. Although very effective, evolutionary algorithms rely heavily on having a large population of individuals (i.e., network architectures) and are therefore memory expensive. In this work, we propose a Regularized Evolutionary Algorithm with low memory footprint to evolve a dynamic image classifier. In details, we introduce novel custom operators that regularize the evolutionary process of a micro-population of 10 individuals. We conduct experiments on three different digits datasets (MNIST, USPS, SVHN) and show that our evolutionary method obtains competitive results with the current state-of-the-art.
| Titolo: | Regularized Evolutionary Algorithm for Dynamic Neural Topology Search | |
| Autori: | ||
| Data di pubblicazione: | 2019 | |
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| Handle: | http://hdl.handle.net/11582/320631 | |
| ISBN: | 978-3-030-30641-0 978-3-030-30642-7 | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| SalRoySeb_ICIAP_2019.pdf | Articolo principale | N/A | NON PUBBLICO - Accesso privato/ristretto | Utenti riconosciuti Richiedi una copia |
http://hdl.handle.net/11582/320631
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