30 citations · 193 across the 34 of their papers we have counts for
5 papers · 1 filter
A General Upper Bound for Unsupervised Domain Adaptation
Dexuan Zhang, Tatsuya Harada
In this work, we present a novel upper bound of target error to address the problem for unsupervised domain adaptation. Recent studies reveal that a deep neural network can learn t…
Revisiting Fine-tuning for Few-shot Learning
Akihiro Nakamura, Tatsuya Harada
Few-shot learning is the process of learning novel classes using only a few examples and it remains a challenging task in machine learning. Many sophisticated few-shot learning alg…
Rethinking Task and Metrics of Instance Segmentation on 3D Point Clouds
Kosuke Arase, Yusuke Mukuta, Tatsuya Harada
Instance segmentation on 3D point clouds is one of the most extensively researched areas toward the realization of autonomous cars and robots. Certain existing studies have split i…
Scalable Generative Models for Graphs with Graph Attention Mechanism
Wataru Kawai, Yusuke Mukuta, Tatsuya Harada
Graphs are ubiquitous real-world data structures, and generative models that approximate distributions over graphs and derive new samples from them have significant importance. Amo…
TWINs: Two Weighted Inconsistency-reduced Networks for Partial Domain Adaptation
Toshihiko Matsuura, Kuniaki Saito, Tatsuya Harada
The task of unsupervised domain adaptation is proposed to transfer the knowledge of a label-rich domain (source domain) to a label-scarce domain (target domain). Matching feature d…