On-device learning has become a new trend for edge intelligence systems. In this paper, we investigate the on-device in-cremental learning problem, which targets to learn new classes on top of a well-trained model on the device. Incremental learning is known to suffer from catastrophic forgetting, i.e., a model learns new classes at the cost of forgetting the old classes. Inspired by model pruning techniques, we propose a new on-device incremental learning method based on weight freezing. The weight freezing in our framework plays two roles: 1) preserving the knowledge of the old classes; 2) boosting the training procedure. By means of weight freezing, we build up an efficient incremental learning framework which combines knowledge distillation to fine-tune the new model. We conduct extensive experiments on CIFAR100 and compare our method with two existing methods. The experimental results show that our method can achieve higher accuracy after incrementally learning new classes.

In this paper, we propose a new module, namely once for all skip (OFAS), for adaptive deep neural networks to efficiently control the block skip within a DNN model. The novelty of OFAS is that it only needs to compute once for all skippable blocks to determine their execution states. Moreover, since adaptive DNN models with OFAS cannot achieve the best accuracy and efficiency in end-to-end training, we propose a reinforcement learning-based training method to enhance the training procedure. The experimental results with different models and datasets demonstrate the effectiveness and efficiency in comparison to the state of the arts. The code is available at https://github.com/ieslab-ynu/OFAS.