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Transfer learning for image classification with sparse prototype representations

Author(s)
Quattoni, Ariadna; Collins, Michael; Darrell, Trevor
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Vision
Advisor
Trevor Darrell
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Abstract
To learn a new visual category from few examples, prior knowledge from unlabeled data as well as previous related categories may be useful.  We develop a new method for transfer learning which exploits available unlabeled data and an arbitrary kernel function; we form a representation based on kernel distances to a large set of unlabeled data points. To transfer knowledge from previous related problems we observe that a category might be learnable using only a small subset of reference prototypes. Related problems may share a significant number of relevant prototypes; we find such a reduced representation by performing a joint loss minimization over the training sets of related problems with a shared regularization penalty that minimizes the total number of prototypes involved in the approximation.This optimization problem can be formulated as a linear program thatcan be solved efficiently. We conduct experiments on a news-topic prediction task where the goal is to predict whether an image belongs to a particularnews topic. Our results show that when only few examples are available for training a target topic, leveraging knowledge learnt from other topics can significantly improve performance.
Date issued
2008-03-03
URI
http://hdl.handle.net/1721.1/40797
Other identifiers
MIT-CSAIL-TR-2008-012
Keywords
transfer learning, image classification

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