Paper Info
Title
Cost-Sensitive Deep Learning with Layer-Wise Cost Estimation.
Abstract
While deep neural networks have succeeded in several visual applications, such as object recognition, detection, and localization, by reaching very high classification accuracies, it is important to note that many real-world applications demand vary- ing costs for different types of misclassification errors, thus requiring cost-sensitive classification algorithms. Current models of deep neural networks for cost-sensitive classification are restricted to some specific network structures and limited depth. In this paper, we propose a novel framework that can be applied to deep neural networks with any structure to facilitate their learning of meaningful representations for cost-sensitive classification problems. Furthermore, the framework allows end- to-end training of deeper networks directly. The framework is designed by augmenting auxiliary neurons to the output of each hidden layer for layer-wise cost estimation, and including the total estimation loss within the optimization objective. Experimental results on public benchmark visual data sets with two cost information settings demonstrate that the proposed frame- work outperforms state-of-the-art cost-sensitive deep learning models.
Year
Venue
Field
2016
arXiv: Computer Vision and Pattern Recognition
Data mining,Data set,Computer science,Deep belief network,Cost estimate,Artificial intelligence,Deep learning,Deep neural networks,Network structure,Pattern recognition,Statistical classification,Machine learning,Cognitive neuroscience of visual object recognition
DocType
Volume
Citations 
Journal
abs/1611.05134
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Yu-An Chung1538.47
Hsuan-Tien Lin282974.77