451 citations · 1.1k across the 28 of their papers we have counts for
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Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
Kuniaki Saito, Kohei Watanabe, Yoshitaka Ushiku +1
In this work, we present a method for unsupervised domain adaptation. Many adversarial learning methods train domain classifier networks to distinguish the features as either a sou…
Adversarial Dropout Regularization
Kuniaki Saito, Yoshitaka Ushiku, Tatsuya Harada +1
We present a method for transferring neural representations from label-rich source domains to unlabeled target domains. Recent adversarial methods proposed for this task learn to a…
Melody Generation for Pop Music via Word Representation of Musical Properties
Andrew Shin, Leopold Crestel, Hiroharu Kato +6
Automatic melody generation for pop music has been a long-time aspiration for both AI researchers and musicians. However, learning to generate euphonious melody has turned out to b…
Spatio-temporal Person Retrieval via Natural Language Queries
Masataka Yamaguchi, Kuniaki Saito, Yoshitaka Ushiku +1
In this paper, we address the problem of spatio-temporal person retrieval from multiple videos using a natural language query, in which we output a tube (i.e., a sequence of boundi…
Asymmetric Tri-training for Unsupervised Domain Adaptation
Kuniaki Saito, Yoshitaka Ushiku, Tatsuya Harada
Deep-layered models trained on a large number of labeled samples boost the accuracy of many tasks. It is important to apply such models to different domains because collecting many…