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Strong-Weak Distribution Alignment for Adaptive Object Detection

Kuniaki Saito, Yoshitaka Ushiku, Tatsuya Harada and Kate Saenko CVPR 2019

We proposed a novel method for domain adaptive object detection. We performed weak-distribution alignment for high-level global feature whereas we also performed strong distribution aignment for low-level local feature. Our method outperformed other baselines with a large margin in four adaptation scenarios.

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Open Set Domain Adaptation by Backpropagation

Kuniaki Saito, Shohei Yamamoto, Yoshitaka Ushiku and Tatsuya Harada, ECCV 2018

We proposed a novel method for open set domain adaptation, where a target domain includes the category absent in a source domain. We propose to give a rejecting option to a classifier and achieve it by adversarial learning. Our method outperforms other methods in various tasks with a large margin.

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Maximum Classifier Discrepancy for Unsupervised Domain Adaptation

Kuniaki Saito, Kohei Watanabe, Yoshitaka Ushiku and Tatsuya Harada, CVPR 2018 oral

We propose a new approach that attempts to align distributions of source and target domain by utilizing the task-specific decision boundaries. We propose to utilize task-specific classifiers as discriminators that try to detect target samples that are far from the support of the source. Our method outperforms other methods on several datasets of image classification and semantic segmentation.

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Adversarial Dropout Reguralization

Kuniaki Saito, Yoshitaka Ushiku, Tatsuya Harada and Kate Saenko, ICLR 2018

We proposed a novel method for unsupervised domain adaptation. The method is based on adversarial learning and effectively utilizes dropout. The method outperforms other methods on digits classification, object classification, and semantic segmentation tasks.

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Assymetric Tri-training for Unsupervised Domain Adaptation

Kuniaki Saito, Yoshitaka Ushiku and Tatsuya Harada ICML 2017

We propose the use of an asymmetric tritraining method for unsupervised domain adaptation, where we assign pseudo-labels to unlabeled samples and train the neural networks as if they are true labels. In our work, we use three networks asymmetrically, and by asymmetric, we mean that two networks are used to label unlabeled target samples, and one network is trained by the pseudo-labeled samples to obtain target-discriminative representations. Our proposed method was shown to achieve a stateof-the-art performance on the benchmark digit recognition datasets for domain adaptation.

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