59 citations · 62 across the 7 of their papers we have counts for
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stat.ML2019★ 1 cited
Domain Discrepancy Measure for Complex Models in Unsupervised Domain Adaptation
Jongyeong Lee, Nontawat Charoenphakdee, Seiichi Kuroki +1
Appropriately evaluating the discrepancy between domains is essential for the success of unsupervised domain adaptation. In this paper, we first point out that existing discrepancy…
stat.ML2019★ 59 cited
On Symmetric Losses for Learning from Corrupted Labels
Nontawat Charoenphakdee, Jongyeong Lee, Masashi Sugiyama
This paper aims to provide a better understanding of a symmetric loss. First, we emphasize that using a symmetric loss is advantageous in the balanced error rate (BER) minimization…