@inproceedings{0411434bbb9d4bffb1b3562e632ec9ae,
title = "On the large-scale transferability of convolutional neural networks",
abstract = "Given the overwhelming performance of the Convolutional Neural Network (CNN) in the computer vision and machine learning community, this paper aims at investigating the effective transfer of the CNN descriptors in generic and fine-grained classification at a large scale. Our contribution consists in providing some simple yet effective methods in constructing a competitive baseline recognition system. Comprehensively, we study two facts in CNN transfer. (1) We demonstrate the advantage of using images with a properly large size as input to CNN instead of the conventionally resized one. (2) We benchmark the performance of different CNN layers improved by average/max pooling on the feature maps. Our evaluation and observation confirm that the Conv5 descriptor yields very competitive accuracy under such a pooling strategy. Following these good practices, we are capable of producing improved performance on seven image classification benchmarks.",
author = "Liang Zheng and Yali Zhao and Shengjin Wang and Jingdong Wang and Yi Yang and Qi Tian",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2018 ; Conference date: 03-06-2018 Through 03-06-2018",
year = "2018",
doi = "10.1007/978-3-030-04503-6_3",
language = "English",
isbn = "9783030045029",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "27--39",
editor = "Mohadeseh Ganji and Lida Rashidi and Fung, {Benjamin C.M.} and Can Wang",
booktitle = "Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2018 Workshops, BDASC, BDM, ML4Cyber, PAISI, DaMEMO, Revised Selected Papers",
address = "Germany",
}