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@TECHREPORT{PrasathElango_Idiap-RR-06-2012,
         author = {Prasath Elango, Sriram and Tommasi, Tatiana and Caputo, Barbara},
       projects = {Idiap, DIRAC},
          month = {1},
          title = {Transfer Learning of Visual Concepts across Robots: a Discriminative Approach},
           type = {Idiap-RR},
         number = {Idiap-RR-06-2012},
           year = {2012},
    institution = {Idiap},
       abstract = {While there is a general consensus that autonomous robots
should be able to learn continuously over time, the learning process
is traditionally envisioned for each specific robot situated in a given
environment. This does not consider the fact that robots performing
similar tasks in similar settings would probably learn similar concepts.
They would therefore benefit by sharing their prior experience with
each other. In this paper we present a transfer learning algorithm that
enables robots located in different places to take advantage of each other’s
experience, boosting the learning process. We specifically assume to have
robots equipped with a camera. We do not make any assumption on
the type of camera, nor on where it is positioned. We also assume that
the robots use the same feature descriptors and learning algorithms.
Under these assumptions, we show that one robot can hugely benefit
from what has been learned by peer robots performing similar tasks. The
advantage concretely means a consistent boost in performance, especially
when training data is scarce. Our algorithm is based on Least Square
Support Vector Machine, and allows to determine automatically from
where to transfer and how much to transfer: this makes it possible
to take advantage of the prior when it is useful, while minimizing the
risk of negative transfer when the priors are not informative. Thorough
experiments on four different publicly available databases show the power
of our approach.},
            pdf = {https://publications.idiap.ch/attachments/reports/2011/PrasathElango_Idiap-RR-06-2012.pdf}
}