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Improving discrimination ability of convolutional neural networks by hybrid learning

Improving discrimination ability of convolutional neural networks by hybrid learning
Kim, IJ[Kim, In-Jung]Choi, C[Choi, Changbeom]Lee, SH[Lee, Sang-Heon]
DGIST Authors
Lee, SH[Lee, Sang-Heon]
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Artificial IntelligenceCharacter RecognitionClassification (of Information)Classification ErrorsConvolutionConvolutional Neural NetworkConvolutional Neural NetworksDeep LearningDiscriminationDiscrimination AbilityErrorsHybrid LearningHybrid Learning AlgorithmLearning AlgorithmsLearning SystemsMachine LearningNeural NetworksPattern RecognitionState-of-the-Art Performance
The discrimination of similar patterns is important because they are the major sources of the classification error. This paper proposes a novel method to improve the discrimination ability of convolutional neural networks (CNNs) by hybrid learning. The proposed method embeds a collection of discriminators as well as a recognizer in a shared CNN. By visualizing contrastive class saliency, we show that learning with embedded discriminators leads the shared CNN to detect and catch the differences among similar classes. Also proposed is a hybrid learning algorithm that learns recognition and discrimination together. The proposed method learns recognition focusing on the differences among similar classes, and thereby improves the discrimination ability of the CNN. Unlike conventional discrimination methods, the proposed method does not require predefined sets of similar classes or additional step to integrate its result with that of the recognizer. In experiments on two handwritten Hangul databases SERI95a and PE92, the proposed method reduced classification error from 2.56 to 2.33, and from 4.04 to 3.66 % respectively. These improvement lead to relative error reduction rates of 8.97 % on SERI95a, and 9.42 % on PE92. Our best results update the state-of-the-art performance which were 4.04 % on SERI95a and 7.08 % on PE92. © 2015, Springer-Verlag Berlin Heidelberg.
Springer Verlag
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Convergence Research Center for Future Automotive Technology 1. Journal Articles


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