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INN: A Method Identifying Clean-annotated Samples via Consistency Effect in Deep Neural Networks
By carrying out various experiments, we demonstrate that the INN method resolves the shortcomings in the memorization effect successfully and thus is helpful to construct more accurate deep prediction models with training data with noisy labels.
Understanding and Improving Virtual Adversarial Training
In semi-supervised learning, virtual adversarial training (VAT) approach is one of the most attractive method due to its intuitional simplicity and powerful performances.
Fast adversarial training for semi-supervised learning
The proposed method generates bad samples of high-quality by use of the adversarial training used in VAT.
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