Non-Linear Domain Adaptation with Boosting
A common assumption in machine vision is that the training and test samples are drawn from the same distribution. However, there are many problems when this assumption is grossly violated, as in bio-medical applications where different acquisitions can generate drastic variations in the appearance of the data due to changing experimental conditions. This problem is accentuated with 3D data, for which annotation is very time-consuming, limiting the amount of data that can be labeled in new acquisitions for training. In this paper we present a multitask learning algorithm for domain adaptation based on boosting. Unlike previous approaches that learn task-specific decision boundaries, our method learns a single decision boundary in a shared feature space, common to all tasks. We use the boosting-trick to learn a non-linear mapping of the observations in each task, with no need for specific a-priori knowledge of its global analytical form. This yields a more parameter-free domain adaptation approach that successfully leverages learning on new tasks where labeled data is scarce. We evaluate our approach on two challenging bio-medical datasets and achieve a significant improvement over the state of the art.
This paper addresses the problem of domain adaptation 
      in bio-medical data based on boosting. The basic idea of the algorithm is 
      to learn a non-linear transformation of different domains to a shared 
      feature space where a single boundary shared across all tasks can be used 
      for classification. The algorithm exploits the boosting trick to handle 
      the non-linearities. The algorithm is applied on two challenging 
      bio-medical datasets, achieving significant performance improvements in 
      both. Overall, I thought that the paper was well written, and the 
      algorithm was novel enough to encourage further research in this 
      direction. While the experiments show good results, I think the 
      experimental evaluation was limited in the set of datasets used, and could 
      be further improved by applying to a larger variety of datasets. Also, 
      some potentially missing baselines and comparisons as mentioned below. 
      Specifically: - It would be good to test the same algorithm on 
      visual datasets such as the one by Saenko et al. Do the same findings hold 
      in that case? - Why isn't the method by Gopalan et al on unsupervised 
      domain adaptation included as another baseline? - What is the running 
      time of your algorithm, for both training and testing? - It would be 
      good to compare your work against the following set of papers (mentioned 
      with Z to be different from paper citations): [Z1] Regularized 
      Multi-Task Learning, KDD 2004 [Z2] Unbiased Look at Dataset Bias, CVPR 
      2011 [Z3] Undoing the Damage of Dataset Bias, ECCV 2012 Z1 is 
      another popular work on multi-task learning that has a common and shared 
      part in a max-margin framework. In addition, the problem seems to be 
      closely related to Z2 and Z3. Specifically, in Z3, the authors also modify 
      multi-task learning formulation for dataset bias (similar to domain 
      adaptation) to learn a single classifier for all 
      datasets.