NDLI: From N to N+1: Multiclass Transfer Incremental Learning

Content Provider	IEEE Xplore Digital Library
Author	Kuzborskij, I. Orabona, F. Caputo, B.
Copyright Year	2013
Description	Author affiliation: Idiap Res. Inst., Martigny, Switzerland (Kuzborskij, I.; Caputo, B.) \|\| Toyota Technol. Inst. at Chicago, Chicago, IL, USA (Orabona, F.)
Abstract	Since the seminal work of Thrun [16], the learning to learn paradigm has been defined as the ability of an agent to improve its performance at each task with experience, with the number of tasks. Within the object categorization domain, the visual learning community has actively declined this paradigm in the transfer learning setting. Almost all proposed methods focus on category detection problems, addressing how to learn a new target class from few samples by leveraging over the known source. But if one thinks of learning over multiple tasks, there is a need for multiclass transfer learning algorithms able to exploit previous source knowledge when learning a new class, while at the same time optimizing their overall performance. This is an open challenge for existing transfer learning algorithms. The contribution of this paper is a discriminative method that addresses this issue, based on a Least-Squares Support Vector Machine formulation. Our approach is designed to balance between transferring to the new class and preserving what has already been learned on the source models. Extensive experiments on subsets of publicly available datasets prove the effectiveness of our approach.
Starting Page	3358
Ending Page	3365
File Size	1200179
Page Count	8
File Format	PDF
ISBN	9780769549897
ISSN	10636919
DOI	10.1109/CVPR.2013.431
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Kernel Visualization Robots Detectors Training data Learning systems leave-one-out transfer learning domain adaptation multiclass visual object categorization LSSVM
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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