Embedding for Out of Vocabulary Words Considering Contextual and Morphosyntactic Information

Won, Min-Sub; Lee, Jee-Hyong

doi:10.1109/ifuzzy.2018.8751687

Cited by 5 publications

(2 citation statements)

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“…Second, we use the extracted intermediate feature set as the observed data of the Gaussian process regression-based detector. The observed dataset for fitting the Gaussian process regression-based detector is as follows: (11) In the application process for the real model, the intermediate feature is extracted through a pre-learned model for a given input image, and the result of the Gaussian process regression is obtained when the extracted value is input. Fig.…”

Section: Gaussian Process Regression-based Detectormentioning

confidence: 99%

“…In recent years, the development of machine learning, especially deep learning, and its applications have been actively studied [1]- [5]. The deep learning model has been applied to various fields, such as image classification [6]- [10], natural language processing [11]- [14], semantic analysis [15] [16], and object detection [17] [18] and shows high performance as a state-of-the-art technique. Although deep learning is applied in many areas, the adversarial attacks that have recently been proposed raise questions about the reliability of the deep learning model.…”

Section: Introductionmentioning

confidence: 99%

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Adversarial Detection with Gaussian Process Regression-based Detector

2019

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Adversarial attack is a technique that causes a malfunction of classification models by adding noise that cannot be distinguished by humans, which poses a threat to a deep learning model. In this paper, we propose an efficient method to detect adversarial images using Gaussian process regression. Existing deep learning-based adversarial detection methods require numerous adversarial images for their training. The proposed method overcomes this problem by performing classification based on the statistical features of adversarial images and clean images that are extracted by Gaussian process regression with a small number of images. This technique can determine whether the input image is an adversarial image by applying Gaussian process regression based on the intermediate output value of the classification model. Experimental results show that the proposed method achieves higher detection performance than the other deep learning-based adversarial detection methods for powerful attacks. In particular, the Gaussian process regression-based detector shows better detection performance than the baseline models for most attacks in the case with fewer adversarial examples.

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Section: Gaussian Process Regression-based Detectormentioning

confidence: 99%