@inproceedings{Rodner13:TBD,
    type = {inproceedings},
    key = {Rodner13:TBD},
    title = {Transform-based Domain Adaptation for Big Data},
    author = {Erik Rodner and Judy Hoffman and Jeff Donahue and Trevor Darrell and Kate Saenko},
    booktitle = {NIPS Workshop on New Directions in Transfer and Multi-Task Learning (NIPS-WS)},
    year = {2013},
    note = {abstract version of arXiv:1308.4200},
    abstract = {Images seen during test time are often not from the same distribution as images used for learning. This problem, known as domain shift, occurs when training classifiers from object-centric internet image databases and trying to apply them directly to scene understanding tasks. The consequence is often severe performance degradation and is one of the major barriers for the application of classi- fiers in real-world systems. In this paper, we show how to learn transform-based domain adaptation classifiers in a scalable manner. The key idea is to exploit an implicit rank constraint, originated from a max-margin domain adaptation formulation, to make optimization tractable. Experiments show that the transformation between domains can be very efficiently learned from data and easily applied to new categories},
    dateadded = {2013-11-22},
    groups = {adaptivelearning,lifelonglearning,mmdt},
}