[2A6-GS-2] Machine learning

[2A6-GS-2-03] Adversarial Training with Data Selection which Improves the Accuracy and Reduces the Computational Complexity of Domain Adaptation

〇Keigo Kimura¹, Daisuke Nakamura¹, Yuta Sakai¹, Goto Masayuki¹ (1. Waseda University)

Keywords:Domain Adaptation, Transfer Learning, Adversarial Training, Image classification, Neural Network

In general, Machine Learning does not ensure the proper prediction if the statistical structures of the features between training data and prediction data are different, but it is sometimes required to apply a prediction model to a target which may have the different structure from its train data. In recent years, the studies to address this challenge have been actively conducted, and one of them is Adversarial Discriminative Domain Adaptation(ADDA), which is the classification model with adversarial training of Generative Adversarial Networks(GAN). ADDA has a defect that it uses all data from mini-batch, which includes bad data for training.
In this study, we propose the improved method of ADDA with the application of GAN's related study, Top-k training. This application would enable ADDA to select useful data based on internal outputs, and the prediction accuracy is expected to increase. The result of the experiment shows that proposed method has significances of the accuracy and the length of training time.