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2016 | OriginalPaper | Chapter

Taxonomy-Regularized Semantic Deep Convolutional Neural Networks

Authors : Wonjoon Goo, Juyong Kim, Gunhee Kim, Sung Ju Hwang

Published in: Computer Vision – ECCV 2016

Publisher: Springer International Publishing

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Abstract

We propose a novel convolutional network architecture that abstracts and differentiates the categories based on a given class hierarchy. We exploit grouped and discriminative information provided by the taxonomy, by focusing on the general and specific components that comprise each category, through the min- and difference-pooling operations. Without using any additional parameters or substantial increase in time complexity, our model is able to learn the features that are discriminative for classifying often confused sub-classes belonging to the same superclass, and thus improve the overall classification performance. We validate our method on CIFAR-100, Places-205, and ImageNet Animal datasets, on which our model obtains significant improvements over the base convolutional networks.

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We also test XOR-pooling that assign 0 to the elements of the children feature maps that are also selected at the parent feature map. However, in our experiments, the XOR-pooling results in a worse performance than diff-pooling, perhaps due to excessive sparsity.

Our experiments reveal that the network is not sensitive to these balancing parameters, as long as the base-level categorization loss has a higher weight than others. That is, \(w_l^m, w_l^d < 1\).

http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html.

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Title: Taxonomy-Regularized Semantic Deep Convolutional Neural Networks
Authors: Wonjoon Goo
Juyong Kim
Gunhee Kim
Sung Ju Hwang
Publisher: Springer International Publishing
Book: Computer Vision – ECCV 2016
Print ISBN: 978-3-319-46474-9

Electronic ISBN: 978-3-319-46475-6

Copyright Year: 2016
DOI: https://doi.org/10.1007/978-3-319-46475-6_6

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