Cross-database transfer learning via learnable and discriminant error-correcting output codes

Feng Ju Chang*, Yen-Yu Lin, Ming Fang Weng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations


We present a transfer learning approach that transfers knowledge across two multi-class, unconstrained domains (source and target), and accomplishes object recognition with few training samples in the target domain. Unlike most of previous work, we make no assumption about the relatedness of these two domains. Namely, data of the two domains can be from different databases and of distinct categories. To overcome the domain variations, we propose to learn a set of commonly-shared and discriminant attributes in form of error-correcting output codes. Upon each of attributes, the unrelated, multi-class recognition tasks of the two domains are transformed into correlative, binary-class ones. The extra source knowledge can alleviate the high risk of overfitting caused by the lack of training data in the target domain. Our approach is evaluated on several benchmark datasets, and leads to about 40% relative improvement in accuracy when only one training sample is available.

Original languageEnglish
Title of host publicationComputer Vision, ACCV 2012 - 11th Asian Conference on Computer Vision, Revised Selected Papers
Number of pages15
EditionPART 1
StatePublished - 11 Apr 2013
Event11th Asian Conference on Computer Vision, ACCV 2012 - Daejeon, Korea, Republic of
Duration: 5 Nov 20129 Nov 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume7724 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference11th Asian Conference on Computer Vision, ACCV 2012
CountryKorea, Republic of

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