  Deep neural networks (DNNs) suffer from noisy-labeled data because of the
risk of overfitting. To avoid the risk, in this paper, we propose a novel DNN
training method with sample selection based on adaptive k-set selection, which
selects k (< n) clean sample candidates from the whole n noisy training samples
at each epoch. It has a strong advantage of guaranteeing the performance of the
selection theoretically. Roughly speaking, a regret, which is defined by the
difference between the actual selection and the best selection, of the proposed
method is theoretically bounded, even though the best selection is unknown
until the end of all epochs. The experimental results on multiple noisy-labeled
datasets demonstrate that our sample selection strategy works effectively in
the DNN training; in fact, the proposed method achieved the best or the
second-best performance among state-of-the-art methods, while requiring a
significantly lower computational cost. The code is available at
https://github.com/songheony/TAkS.

Machine Learning;Machine Learning