Learning Convolutional Neural Networks in presence of Concept Drift
Simone Disabato, Manuel Roveri · 2019
Designing adaptive machine learning systems able to operate in nonstationary conditions, also called concept drift, is a novel and promising research area. Convolutional Neural Networks (CNNs) have not been considered a viable solution for such adaptive systems due to the high computational load and the high number of images they require for the training. This paper introduces an adaptive mechanism for learning CNNs able to operate in presence of concept drift. Such an adaptive mechanism follows an "active approach", where the adaptation is triggered by the detection of a concept drift, and relies on the "transfer learning" paradigm to transfer (part of the) knowledge from the CNN operating before the concept drift to the one operating after. The effectiveness of the proposed solution has been evaluated on two types of CNNs and two real-world image benchmarks.