Beyond Dataset Bias: Multi-task Unaligned Shared Knowledge Transfer

  • Tatiana Tommasi
  • Novi Quadrianto
  • Barbara Caputo
  • Christoph H. Lampert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7724)


Many visual datasets are traditionally used to analyze the performance of different learning techniques. The evaluation is usually done within each dataset, therefore it is questionable if such results are a reliable indicator of true generalization ability. We propose here an algorithm to exploit the existing data resources when learning on a new multiclass problem. Our main idea is to identify an image representation that decomposes orthogonally into two subspaces: a part specific to each dataset, and a part generic to, and therefore shared between, all the considered source sets. This allows us to use the generic representation as un-biased reference knowledge for a novel classification task. By casting the method in the multi-view setting, we also make it possible to use different features for different databases. We call the algorithm MUST, Multitask Unaligned Shared knowledge Transfer. Through extensive experiments on five public datasets, we show that MUST consistently improves the cross-datasets generalization performance.


Projection Matrix Domain Adaptation Distribution Mismatch Transfer Learn Projection Matrice 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Tatiana Tommasi
    • 1
    • 2
  • Novi Quadrianto
    • 3
  • Barbara Caputo
    • 1
  • Christoph H. Lampert
    • 4
  1. 1.Idiap Research InstituteMartignySwitzerland
  2. 2.École Polytechnique Fédérale de LausanneSwitzerland
  3. 3.University of CambridgeUK
  4. 4.IST Austria (Institute of Science and Technology Austria)KlosterneuburgAustria

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