NDLI: Learning with dataset bias in latent subcategory models

Content Provider	IEEE Xplore Digital Library
Author	Stamos, D. Martelli, S. Nabi, M. McDonald, A. Murino, V. Pontil, M.
Copyright Year	2015
Description	Author affiliation: Dept. of Comput. Sci., Univ. Coll. London, London, UK (Stamos, D.; McDonald, A.; Pontil, M.) \|\| Pattern Anal. & Comput. Vision, Ist. Italiano di Tecnol., Genoa, Italy (Martelli, S.; Nabi, M.; Murino, V.)
Abstract	Latent subcategory models (LSMs) offer significant improvements over training linear support vector machines (SVMs). Training LSMs is a challenging task due to the potentially large number of local optima in the objective function and the increased model complexity which requires large training set sizes. Often, larger datasets are available as a collection of heterogeneous datasets. However, previous work has highlighted the possible danger of simply training a model from the combined datasets, due to the presence of bias. In this paper, we present a model which jointly learns an LSM for each dataset as well as a compound LSM. The method provides a means to borrow statistical strength from the datasets while reducing their inherent bias. In experiments we demonstrate that the compound LSM, when tested on PASCAL, LabelMe, Caltech101 and SUN09 in a leave-one-dataset-out fashion, achieves an average improvement of over 6.5% over a previous SVM-based undoing bias approach and an average improvement of over 8.5% over a standard LSM trained on the concatenation of the datasets.
Starting Page	3650
Ending Page	3658
File Size	750314
Page Count	9
File Format	PDF
ISSN	10636919
e-ISBN	9781467369640
DOI	10.1109/CVPR.2015.7298988
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Standards Linear programming Visualization Support vector machines Computational modeling Compounds
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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