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         author = {Tommasi, Tatiana and Orabona, Francesco and Caputo, Barbara},
       projects = {Idiap, DIRAC, EMMA},
          month = {4},
          title = {Learning Categories from Few Examples with Multi Model Knowledge Transfer},
           type = {Idiap-RR},
        journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
         number = {Idiap-RR-16-2013},
           year = {2013},
    institution = {Idiap},
       abstract = {Learning a visual object category from few samples is a compelling and challenging problem. In several real-world
applications collecting many annotated data is costly and not always possible. However a small training set does not allow to cover the
high intraclass variability typical of visual objects. In this condition, machine learning methods provide very few guarantees. This paper
presents a discriminative model adaptation algorithm able to proficiently learn a target object with few examples by relying on other
previously learned source categories. The proposed method autonomously chooses from where and how much to transfer information
by solving a convex optimization problem which ensures to have the minimal leave-one-out error on the available training set. We
analyze several properties of the described approach and perform an extensive experimental comparison with other existing transfer
solutions, consistently showing the value of our algorithm.},
            pdf = {https://publications.idiap.ch/attachments/reports/2013/Tommasi_Idiap-RR-16-2013.pdf}