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A Convolutional Neural Network (CNN) is sometimes confronted with objects ofchanging appearance ( new instances) that exceed its generalization capability.This requires the CNN to incorporate new knowledge, i.e., to learnincrementally. In this paper, we are concerned with this problem in the contextof assisted living. We propose using the feature space that results from thetraining dataset to automatically label problematic images that could not beproperly recognized by the CNN. The idea is to exploit the extra information inthe feature space for a semi-supervised labeling and to employ problematicimages to improve the CNN's classification model. Among other benefits, theresulting semi-supervised incremental learning process allows improving theclassification accuracy of new instances by 40% as illustrated by extensiveexperiments.

A CNN-based Feature Space for Semi-supervised Incremental Learning in Assisted Living Applications