Deep convolutional neural network (DCNN) based supervised learning is a widely practiced approach for large-scale image classification. However, retraining these large networks to accommodate new, previously unseen data demands high computational time and energy requirements. Also, previously seen training samples may not be available at the time of retraining. We propose an efficient training methodology and incrementally growing DCNN to learn new tasks while sharing part of the base network. Our proposed methodology is inspired by transfer learning techniques, although it does not forget previously learned tasks. An updated network, for learning new set of classes, is formed using previously learned convolutional layers (shared from initial part of base network) with addition of few newly added convolutional kernels included in the later layers of the network. We employed a 'clone-and-branch' technique with calibration, which allows the network to learn new tasks (containing classes with similar features as old tasks) one after another without any performance loss in old tasks. We evaluated the proposed scheme on several recognition applications. The classification accuracy achieved by our approach is comparable to the regular incremental learning approach (where networks are updated with new training samples only, without any network sharing), while achieving energy efficiency, reduction in storage requirements, memory access and training time. Incremental learning, catastrophic forgetting, lifelong learning, energy-efficient learning, network sharing. SYED SHAKIB SARWAR received the B.Sc. and M.Sc. degrees in electrical and electronic engineering from the Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh, in 2012 and 2014, respectively. He is currently pursuing the Ph.D. degree with Purdue University under the supervision of Prof. K. Roy. In Fall 2014, he joined the School of Electrical and Computer Engineering, Purdue University. In Summer 2018, he worked as a Research Intern in Facebook Reality Labs, where he developed optimization techniques for machine learning applications. Since May 2019, he has been working with Facebook AR/VR as a Research Scientist. His primary researches are focus on energy efficient algorithms and hardware co-design for machine learning applications (deep learning) based on CMOS and emerging devices. His research interest also includes approximate computing in the field of deep learning.
In REF the network incrementally grows over time while sharing portions of the base module.
Incremental Learning in Deep Convolutional Neural Networks Using Partial Network Sharing
IEEE Access
IEEE Access
Computer Science