@inproceedings{8e3106c1183b49d49cfac916433b0844,
title = "Auto data augmentation for testing set",
abstract = "Testing phase augmentation is a fast way to further improve the performance of image classification when CNN (Convolutional Neural Network) is already trained for hours. Limited attempts have been made to find the best augmentation strategy for testing set. We propose a reinforcement learning based augmentation strategy searching method for testing phase augmentation. With the augmentation strategy, we augment each testing image and integrate features of its augmented images into one feature. The reinforcement learning method searches the best parameters in the augmentation strategy which is formed as a matrix in this paper. Using the proposed method, we achieve competitive accuracies on image classification and face verification.",
keywords = "Deep reinforcement learning, Face verification, Image augmentation",
author = "Wanshun Gao and Xi Zhao",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 2nd Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2019 ; Conference date: 08-11-2019 Through 11-11-2019",
year = "2019",
doi = "10.1007/978-3-030-31723-2\_6",
language = "英语",
isbn = "9783030317225",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "66--78",
editor = "Zhouchen Lin and Liang Wang and Tieniu Tan and Jian Yang and Guangming Shi and Nanning Zheng and Xilin Chen and Yanning Zhang",
booktitle = "Pattern Recognition and Computer Vision 2nd Chinese Conference, PRCV 2019, Proceedings, Part II",
}