NDLI: Channel-Max, Channel-Drop and Stochastic Max-pooling

Content Provider	IEEE Xplore Digital Library
Author	Yuchi Huang Xiuyu Sun Ming Lu Ming Xu
Copyright Year	2015
Description	Author affiliation: Tsinghua Univ., Beijing, China (Ming Lu) \|\| NEC Labs., Beijing, China (Yuchi Huang; Xiuyu Sun; Ming Xu)
Abstract	We propose three regularization techniques to overcome drawbacks of local winner-take-all methods used in deep convolutional networks. Channel-Max inherits the max activation unit from Maxout networks, but otherwise adopts complementary subsets of input and filters with different kernel sizes as better companions to the max function. To balance the training on different pathways, Channel-Drop is employed to randomly discard half pathways before their inputs are convolved respectively. Stochastic Max-pooling is defined to reduce the overfitting caused by conventional max-pooling, in which half activations are randomly dropped in each pooling region during training and top largest activations are probabilistically averaged during testing. Using Channel-Max, Channel-Drop and Stochastic Max-pooling, we demonstrate state-of-the-art performance on four benchmark datasets: CIFAR-10, CIFAR-100, STL-10 and SVHN.
Starting Page	9
Ending Page	17
File Size	568478
Page Count	9
File Format	PDF
ISSN	21607516
e-ISBN	9781467367592
DOI	10.1109/CVPRW.2015.7301267
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Convolution Stochastic processes Color Mathematical model Kernel Testing
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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