3 citations · 8 across the 7 of their papers we have counts for
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Empirical Study on Optimizer Selection for Out-of-Distribution Generalization
Hiroki Naganuma, Kartik Ahuja, Shiro Takagi +5
Modern deep learning systems do not generalize well when the test data distribution is slightly different to the training data distribution. While much promising work has been acco…
Optimal transport meets noisy label robust loss and MixUp regularization for domain adaptation
Kilian Fatras, Hiroki Naganuma, Ioannis Mitliagkas
It is common in computer vision to be confronted with domain shift: images which have the same class but different acquisition conditions. In domain adaptation (DA), one wants to c…
Conjugate Gradient Method for Generative Adversarial Networks
Hiroki Naganuma, Hideaki Iiduka
One of the training strategies of generative models is to minimize the Jensen--Shannon divergence between the model distribution and the data distribution. Since data distribution…