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End-to-End Lifelong Learning: a Framework to Achieve Plasticities of both the Feature and Classifier Constructions

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Abstract

Plasticity in our brain offers us promising ability to learn and know the world. Although great successes have been achieved in many fields, few bio-inspired machine learning methods have mimicked this ability. Consequently, when meeting large-scale or time-varying data, these bio-inspired methods are infeasible, due to the reasons that they lack plasticity and need all training data loaded into memory. Furthermore, even the popular deep convolutional neural network (CNN) models have relatively fixed structures and cannot process time varying data well. Through incremental methodologies, this paper aims at exploring an end-to-end lifelong learning framework to achieve plasticities of both the feature and classifier constructions. The proposed model mainly comprises of three parts: Gabor filters followed by max pooling layer offering shift and scale tolerance to input samples, incremental unsupervised feature extraction, and incremental SVM trying to achieve plasticities of both the feature learning and classifier construction. Different from CNN, plasticity in our model has no back propogation (BP) process and does not need huge parameters. Our incremental models, including IncPCANet and IncKmeansNet, have achieved better results than PCANet and KmeansNet on minist and Caltech101 datasets respectively. Meanwhile, IncPCANet and IncKmeansNet show promising plasticity of feature extraction and classifier construction when the distribution of data changes. Lots of experiments have validated the performance of our model and verified a physiological hypothesis that plasticity exists in high level layer better than that in low level layer.

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Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 61773375, Grant 61375036, and Grant 61511130079, in part by the Microsoft Collaborative Research Project.

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    Authors and Affiliations

    1. Institute of Automation, University of Chinese Academy of Sciences (UCAS), Beijing, China

      Wangli Hao, Junsong Fan, Zhaoxiang Zhang & Guibo Zhu

    2. CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Beijing, China

      Zhaoxiang Zhang

    3. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (NLPR, CASIA), Beijing, China

      Zhaoxiang Zhang

    Authors
    1. Wangli Hao
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    2. Junsong Fan
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    3. Zhaoxiang Zhang
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    4. Guibo Zhu
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    Correspondence to Zhaoxiang Zhang.

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    This article does not contain any studies with human participants or animals performed by any of the authors.

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    Wangli Hao and Junsong Fan contribute equally to this study and share first authorship.

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    Hao, W., Fan, J., Zhang, Z. et al. End-to-End Lifelong Learning: a Framework to Achieve Plasticities of both the Feature and Classifier Constructions. Cogn Comput 10, 321–333 (2018). https://doi.org/10.1007/s12559-017-9514-0

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