Takashi Ishida | DeepAI

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∙ 02/01/2022

Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classification

There is a fundamental limitation in the prediction performance that a m...

0 Takashi Ishida, et al. ∙

∙ 03/01/2021

LocalDrop: A Hybrid Regularization for Deep Neural Networks

In neural networks, developing regularization algorithms to settle overf...

0 Ziqing Lu, et al. ∙

∙ 02/20/2020

Do We Need Zero Training Loss After Achieving Zero Training Error?

Overparameterized deep networks have the capacity to memorize training d...

0 Takashi Ishida, et al. ∙

∙ 10/10/2018

Complementary-Label Learning for Arbitrary Losses and Models

In contrast to the standard classification paradigm where the true (or p...

0 Takashi Ishida, et al. ∙

∙ 10/19/2017

Binary Classification from Positive-Confidence Data

Reducing labeling costs in supervised learning is a critical issue in ma...

0 Takashi Ishida, et al. ∙

∙ 05/22/2017

Learning from Complementary Labels

Collecting labeled data is costly and thus a critical bottleneck in real...

0 Takashi Ishida, et al. ∙

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