How Robots Learn to Classify New Objects Trained from Small Data Sets

Kurzfassung

In this paper, we address the problem of learning to classify new object classes and instances by adapting a previously trained classifier. The main challenges here are the small amount of newly available training data and the large change in appearance between the new and the old data. To address this we propose a new variant of Progressive Neural Networks (PNN), originally introduced by Rusu et al. [1]. We show that by performing a specific simplification in the adapters, the prediction performance of the resulting PNN can be significantly increased. Furthermore, we give additional insights about when PNNs outperform alternative methods, and provide empirical evaluations on benchmark datasets. Finally, we also suggests a way of using it to augment the functionality of a network by extending it with new classes, addressing the problem of unbalanced classes, i.e. where the new classes are under-represented.