  Domain adaptation is to transfer the shared knowledge learned from the source
domain to a new environment, i.e., target domain. One common practice is to
train the model on both labeled source-domain data and unlabeled target-domain
data. Yet the learned models are usually biased due to the strong supervision
of the source domain. Most researchers adopt the early-stopping strategy to
prevent over-fitting, but when to stop training remains a challenging problem
since the lack of the target-domain validation set. In this paper, we propose
one efficient bootstrapping method, called Adaboost Student, explicitly
learning complementary models during training and liberating users from
empirical early stopping. Adaboost Student combines the deep model learning
with the conventional training strategy, i.e., adaptive boosting, and enables
interactions between learned models and the data sampler. We adopt one adaptive
data sampler to progressively facilitate learning on hard samples and aggregate
"weak" models to prevent over-fitting. Extensive experiments show that (1)
Without the need to worry about the stopping time, AdaBoost Student provides
one robust solution by efficient complementary model learning during training.
(2) AdaBoost Student is orthogonal to most domain adaptation methods, which can
be combined with existing approaches to further improve the state-of-the-art
performance. We have achieved competitive results on three widely-used scene
segmentation domain adaptation benchmarks.

Computer Vision and Pattern Recognition