HiFI: A Hierarchical Framework for Incremental Learning using Deep Feature Representation The presented work focuses on automatic recognition of object classes while ensuring near realtime training required for recognizing a new object not seen previously. This is achieved by proposing a twostage hierarchical deep learning framework which first learns object categories using a Nearest Class Mean (NCM) classifier applied directly to CNN features and then, uses a twolayer artificial neural network to learn the object labels within each category. In order to recognize a new object not seen earlier, the category is identified first and then the second stage neural network is incrementally trained with the features of the new object without forgetting previously learnt labels. The proposed hierarchical framework is shown to provide comparable recognition accuracy with significant reduction in overall computational time in recognizing new objects compared to methods that use endtoend retraining. The efficacy of the approach is demonstrated through comparison with existing stateoftheart methods on the publicly available CORe50 dataset.

Broad Learning System for Class Incremental Learning The largescale image datasets such as ImageNet and openended photo websites are revealing new challenges to image classification that were not apparent in smaller and fixed sets. In particular, how to handle the dynamically growing datasets efficiently, where not only the amount of training data but also the number of classes increases over time, remains an unexplored problem. In this challenging setting, we study how to employ the Broad Learning System (BLS) to deal with the incremental increases of sample classes in datasets. We first determine whether a new batch of samples is from certain seen classes or unseen classes by two methods based on the statistics theory. Then, the incremental training algorithms are developed to classify unlearned data from new classes. We test our class incremental learning algorithms of BLS on some representative datasets such as Cifar100. The experimental results show that our methods can accurately distinguish seen and unseen classes. And the comparison study demonstrates that our method can be extended to 100 classes on Cifar100 with an acceptable loss of accuracy and performs better compared to other similar class incremental algorithms such as iCaRL.