3D_DEN: Open-Ended 3-D Object Recognition Using Dynamically Expandable Networks

Abstract

Service robots, in general, have to work independently and adapt to the dynamic changes in the environment. One important aspect in such scenarios is to continually learn to recognize newer object categories when they become available. This combines two main research problems namely continual learning and 3D object recognition. Most of the existing research approaches include the use of deep Convolutional Neural Networks (CNNs) focusing on image datasets. A modified approach might be needed for continually learning 3D object categories. A major concern in using CNNs is the problem of catastrophic forgetting when a model tries to learn a new task. Despite various recent proposed solutions to mitigate this problem, there still exist a few side-effects (such as time/computational complexity) of such solutions. We propose a model capable of learning 3D object categories in an open-ended fashion by employing a deep transfer learning-based approach combined with dynamically expandable layers, which also makes sure that these side-effects are minimized to a great extent. We show that this model sets a new state-of-the-art standard with regards to accuracy and also substantially minimizes computational complexity.