Papers › Adversarial Multiple Source Domain Adaptation

Adversarial Multiple Source Domain Adaptation

1 Dec 2018NeurIPS 2018 12archive 2025-07-28

Han Zhao, Shanghang Zhang, Guanhang Wu, José M. F. Moura, Joao P. Costeira, Geoffrey J. Gordon

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.

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Tasks

Classification Domain Adaptation General Classification Generalization Bounds Multi-Source Unsupervised Domain Adaptation Sentiment Analysis regression

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Domain Adaptation	GTA5+Synscapes to Cityscapes	MDAN	mIoU	55.2	#3 of 5	Archive leaderboard	report
Domain Adaptation	GTAV+Synscapes to Cityscapes	MDAN	mIoU	55.2	#4 of 6	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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