GENERALIZATION BOUNDS FOR DOMAIN ADAPTATION VIA DOMAIN TRANSFORMATIONS

Date

2018-09-20

Author

Vural, Elif

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Many domain adaptation methods are based on learning a projection or a transformation of the source and target domains to a common domain and training a classifier there, while the performance of such algorithms has not been theoretically studied yet. Previous studies proposing generalization bounds for domain adaptation relate the target loss to the discrepancy between the source and target distributions, however, do not take into account the possible effects of learning a transformation between the two domains. In this work, we present generalization bounds that study the target performance of domain adaptation methods learning a transformation of the source and target domains along with a hypothesis. We show that, under some conditions on the loss regularity, if the domain transformations reduce the distribution distance at a sufficiently high rate, then the expected target loss can be bounded with probability improving at an exponential rate with the number of labeled samples.

Subject Keywords

Domain adaptation, Domain transformations, Performance analysis, Generalization bounds

URI

https://hdl.handle.net/11511/52582

Conference Name

IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP)

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

E. Vural, “GENERALIZATION BOUNDS FOR DOMAIN ADAPTATION VIA DOMAIN TRANSFORMATIONS,” presented at the IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP), Aalborg, DENMARK, 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/52582.