Incremental On Line Learning of Object Classes using a Combination of Self Organizing Incremental Neural Networks and Deep Convolutional Neural Networks*
This paper reports initial results from our ongoing work on Natural Language grounding through situated interaction for robots acting in unstructured environments. We present an approach for incremental on line learning of object classes based on a novel combination of a Self Organizing Incremental Neural Network (SOINN) and a deep Convolutional Neural Network (CNN) We evaluate our approach on the RGB D Object Dataset and on four aspects: 1) object classification accuracy, 2) incremental learning in terms of the number of classes, 3) potential for realising active learning, and 4) feasibility to use the system in an on line setting. Our results show that the approach has an acceptable performance for object classification (e.g. over 90% accuracy) and provide some insights into how it could be improved by harnessing interaction with both the environment and a human tutor.