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Lifelong Learning via Progressive Distillation and Retrospection

  • Saihui Hou
  • Xinyu Pan
  • Chen Change Loy
  • Zilei Wang
  • Dahua Lin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11207)

Abstract

Lifelong learning aims at adapting a learned model to new tasks while retaining the knowledge gained earlier. A key challenge for lifelong learning is how to strike a balance between the preservation on old tasks and the adaptation to a new one within a given model. Approaches that combine both objectives in training have been explored in previous works. Yet the performance still suffers from considerable degradation in a long sequence of tasks. In this work, we propose a novel approach to lifelong learning, which tries to seek a better balance between preservation and adaptation via two techniques: Distillation and Retrospection. Specifically, the target model adapts to the new task by knowledge distillation from an intermediate expert, while the previous knowledge is more effectively preserved by caching a small subset of data for old tasks. The combination of Distillation and Retrospection leads to a more gentle learning curve for the target model, and extensive experiments demonstrate that our approach can bring consistent improvements on both old and new tasks (Project page: http://mmlab.ie.cuhk.edu.hk/projects/lifelong/).

Keywords

Lifelong learning Knowledge distillation Retrospection 

Notes

Acknowledgment

This work is partially supported by the NSFC under Grant 61673362, Youth Innovation Promotion Association CAS, and the Fundamental Research Funds for the Central Universities. This work is also partially supported by the Big Data Collaboration Research grant from SenseTime Group (CUHK Agreement No. TS1610626), the General Research Fund (GRF) of Hong Kong (No. 14236516, 14241716, 14224316, 14209217).

Supplementary material

474178_1_En_27_MOESM1_ESM.pdf (272 kb)
Supplementary material 1 (pdf 272 KB)

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of AutomationUniversity of Science and Technology of ChinaHefeiChina
  2. 2.Department of Information EngineeringThe Chinese University of Hong KongHong KongChina
  3. 3.Nanyang Technological UniversitySingaporeSingapore

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