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Knowledge-guided semantic computing network
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DOI：10.1016/j.neucom.2020.09.075.png)
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Abstract
En 中文
The excellent performance of deep neural networks mainly relies on the attributes of the dataset, such as the size, diversity, completeness. However, it is usually difficult to obtain a high-qualified training dataset for some scenarios. Inspired by the human visual cognition process with few sample learning, and strong robustness, we believe the experience knowledge is more powerful than large-scale data. To combine the power of knowledge and data, we propose a knowledge-guided semantic computing network (SCN) in this paper, which is constructed with a primary knowledge-guided semantic tree module and an auxiliary data-driven lightweight neural network module. The semantic tree module can calculate the classification results by a forward computing process rapidly. The lightweight neural network module can aid the semantic tree module for higher classification ability. We also propose a hinge cross-entropy loss function to train the SCN, which enables the SCN to focus on those misclassified training samples and further improve the classification accuracy. The experimental results on MNIST and GTSRB data sets prove that the SCN achieves excellent classification accuracy comparable to the state-of-the-art methods on the original training samples and higher classification accuracy than the state-of-the-art methods on few training samples. What is more, at BIM eps = 0.3 on MNIST and FGSM eps = 0.03 on GTSRB adversarial test samples, the proposed SCN(1/4) and SCN(1/8) obtain over 75% and 14% accuracy improvement than the original CapsNet. (C) 2020 Elsevier B.V. All rights reserved.
Keywords:
Semantic computing network (SCN)
Semantic tree
Lightweight neural network
Adversarial attacks
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