In the last decade, Convolutional Neural Networks (CNNs) have shown to perform incredibly well in many computer vision tasks such as object recognition and object detection, being able to extract meaningful high-level invariant features. However, partly because of their complex training and tricky hyper-parameters tuning, CNNs have been scarcely studied in the context of incremental learning where data are available in consecutive batches and retraining the model from scratch is unfeasible. In this work we compare different incremental learning strategies for CNN based architectures, targeting real-word applications.
Comparing Incremental Learning Strategies for Convolutional Neural Networks / Lomonaco, Vincenzo; Maltoni, Davide. - ELETTRONICO. - 9896:(2016), pp. 175-184. (Intervento presentato al convegno 7th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2016 tenutosi a deu nel 2016) [10.1007/978-3-319-46182-3_15].
Comparing Incremental Learning Strategies for Convolutional Neural Networks
LOMONACO, VINCENZO;MALTONI, DAVIDE
2016
Abstract
In the last decade, Convolutional Neural Networks (CNNs) have shown to perform incredibly well in many computer vision tasks such as object recognition and object detection, being able to extract meaningful high-level invariant features. However, partly because of their complex training and tricky hyper-parameters tuning, CNNs have been scarcely studied in the context of incremental learning where data are available in consecutive batches and retraining the model from scratch is unfeasible. In this work we compare different incremental learning strategies for CNN based architectures, targeting real-word applications.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
