Research Area:  Machine Learning

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.

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Author(s) Name:  Vincenzo Lomonaco & Davide Maltoni

Publisher name:  Springer

DOI:  10.1007/978-3-319-46182-3_15

Volume Information:  

Paper Link:   https://link.springer.com/chapter/10.1007/978-3-319-46182-3_15