Poster
Adversarial Multiple Source Domain Adaptation
Han Zhao · Shanghang Zhang · Guanhang Wu · José M. F. Moura · Joao P Costeira · Geoffrey Gordon

Tue Dec 4th 10:45 AM -- 12:45 PM @ Room 517 AB #107

While domain adaptation has been actively researched, most algorithms focus on the single-source-single-target adaptation setting. In this paper we propose new generalization bounds and algorithms under both classification and regression settings for unsupervised multiple source domain adaptation. Our theoretical analysis naturally leads to an efficient learning strategy using adversarial neural networks: we show how to interpret it as learning feature representations that are invariant to the multiple domain shifts while still being discriminative for the learning task. To this end, we propose multisource domain adversarial networks (MDAN) that approach domain adaptation by optimizing task-adaptive generalization bounds. To demonstrate the effectiveness of MDAN, we conduct extensive experiments showing superior adaptation performance on both classification and regression problems: sentiment analysis, digit classification, and vehicle counting.

Author Information

Han Zhao (Carnegie Mellon University)
Shanghang Zhang (Carnegie Mellon University)
Guanhang Wu (Carnegie Mellon University)
José M. F. Moura (Carnegie Mellon University)
Joao P Costeira (Instituto Superior Tecnico VAT- 501507930)
Geoffrey Gordon (MSR Montréal & CMU)

Dr. Gordon is an Associate Research Professor in the Department of Machine Learning at Carnegie Mellon University, and co-director of the Department's Ph. D. program. He works on multi-robot systems, statistical machine learning, game theory, and planning in probabilistic, adversarial, and general-sum domains. His previous appointments include Visiting Professor at the Stanford Computer Science Department and Principal Scientist at Burning Glass Technologies in San Diego. Dr. Gordon received his B.A. in Computer Science from Cornell University in 1991, and his Ph.D. in Computer Science from Carnegie Mellon University in 1999.

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