Search Results for author: Takashi Ishida

Found 6 papers, 5 papers with code

LocalDrop: A Hybrid Regularization for Deep Neural Networks

no code implementations1 Mar 2021 Ziqing Lu, Chang Xu, Bo Du, Takashi Ishida, Lefei Zhang, Masashi Sugiyama

In neural networks, developing regularization algorithms to settle overfitting is one of the major study areas.

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

1 code implementation ICML 2020 Takashi Ishida, Ikko Yamane, Tomoya Sakai, Gang Niu, Masashi Sugiyama

We experimentally show that flooding improves performance and, as a byproduct, induces a double descent curve of the test loss.


Complementary-Label Learning for Arbitrary Losses and Models

1 code implementation Proceedings of the 36th International Conference on Machine Learning, 2019 Takashi Ishida, Gang Niu, Aditya Krishna Menon, Masashi Sugiyama

In contrast to the standard classification paradigm where the true class is given to each training pattern, complementary-label learning only uses training patterns each equipped with a complementary label, which only specifies one of the classes that the pattern does not belong to.

General Classification Image Classification

Learning from Complementary Labels

1 code implementation NeurIPS 2017 Takashi Ishida, Gang Niu, Weihua Hu, Masashi Sugiyama

Collecting complementary labels would be less laborious than collecting ordinary labels, since users do not have to carefully choose the correct class from a long list of candidate classes.

Classification General Classification +1

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