4 citations · 5 across the 2 of their papers we have counts for
2 papers
cs.LG2021★ 1 cited
Learning to Cascade: Confidence Calibration for Improving the Accuracy and Computational Cost of Cascade Inference Systems
Shohei Enomoto, Takeharu Eda
Recently, deep neural networks have become to be used in a variety of applications. While the accuracy of deep neural networks is increasing, the confidence score, which indicates…
stat.ML2019★ 4 cited
Effective Data Augmentation with Multi-Domain Learning GANs
Shin'ya Yamaguchi, Sekitoshi Kanai, Takeharu Eda
For deep learning applications, the massive data development (e.g., collecting, labeling), which is an essential process in building practical applications, still incurs seriously…