Learning without forgetting

Zhizhong Li, Derek Hoiem

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

When building a unified vision system or gradually adding new capabilities to a system, the usual assumption is that training data for all tasks is always available. However, as the number of tasks grows, storing and retraining on such data becomes infeasible. A new problem arises where we add new capabilities to a Convolutional Neural Network (CNN), but the training data for its existing capabilities are unavailable. We propose our Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities. Our method performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques and performs similarly to multitask learning that uses original task data we assume unavailable. A more surprising observation is that Learning without Forgetting may be able to replace fine-tuning as standard practice for improved new task performance.

Original languageEnglish (US)
Title of host publicationComputer Vision - 14th European Conference, ECCV 2016, Proceedings
EditorsBastian Leibe, Jiri Matas, Nicu Sebe, Max Welling
PublisherSpringer
Pages614-629
Number of pages16
ISBN (Print)9783319464923
DOIs
StatePublished - 2016
Event14th European Conference on Computer Vision, ECCV 2016 - Amsterdam, Netherlands
Duration: Oct 11 2016Oct 14 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9908 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other14th European Conference on Computer Vision, ECCV 2016
CountryNetherlands
CityAmsterdam
Period10/11/1610/14/16

Keywords

  • Convolutional neural networks
  • Deep learning
  • Multitask learning
  • Transfer learning
  • Visual recognition

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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