Explainable deep learning for efficient and robust pattern recognition: A survey of recent developments

Bai, Xiao; Wang, Xiang; Liu, Xianglong; Liu, Qiang; Song, Jingkuan; Sebe, Nicu; Kim, Been

doi:10.1016/j.patcog.2021.108102

Cited by 194 publications

(72 citation statements)

References 36 publications

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“…For the sake of verifying the effectiveness of the proposed privacy protection method, this study verifies it on three datasets: LFW, SFC, and YTF and compares it with DPSGD and PATE privacy protection methods [ 27 ]. The results are represented in Table 4 [ 17 ].…”

Section: Results and Analysismentioning

confidence: 99%

Research on the Privacy Security of Face Recognition Technology

Pang

2022

Computational Intelligence and Neuroscience

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To solve the problem that privacy data are easy to leak in the application of face recognition technology in apps, a method which is based on differential privacy for privacy security protection is proposed. Firstly, Bayesian GAN is conducted to obtain the training data with the same distribution as the privacy data, and the algorithm of differential privacy is conducted to train the training data to obtain these labels with privacy protection. Then, based on the proposed lightface lightweight face recognition model, the tag with noise is generated, and the gradient descent is conducted on the recovered face feature vector from the attack. Finally, through the analysis of privacy loss, an accurate privacy protection boundary is provided. From the results of experiments, it could be known that the proposed privacy security protection method can effectively protect the parameter information of the face recognition model under the face recognition technology and reduce the recognition accuracy of the image recovered by the attacker. Compared with the privacy protection methods such as DPSGD and PATE, it has strong privacy protection ability and can be applied to the privacy protection of practical APP.

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Section: Results and Analysismentioning

confidence: 99%

Research on the Privacy Security of Face Recognition Technology

Pang

2022

Computational Intelligence and Neuroscience

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“…The eigenvectors A ( a 1 , a 2 ,…, a n ) and B ( b 1 , b 2 ,…, b n ) are known, and the Euclidean distance between them is expressed as d euc ( A , B ), and the specific formula is as follows [ 27 ]:

…”

Section: Cultural Relic Recognition Based On Self-attention Concentrationmentioning

confidence: 99%

“…In the experiment, the necessary environment needs to be configured first, which is mainly divided into two parts: hardware and software. The hardware information is shown in Table 1 [ 27 ].…”

Section: Test Verificationmentioning

confidence: 99%

Museum Relic Image Detection and Recognition Based on Deep Learning

Wang

2022

Computational Intelligence and Neuroscience

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To improve the accuracy of museum cultural relic image recognition, the DenseNet and ResNet are selected as the backbone neural networks for detection and recognition. In view of the small target problem in cultural relics, the feature pyramid is introduced in this paper to improve the DenseNet method. The accuracy of target detection is improved through multiscale feature extraction and fusion. At the same time, aiming the problem of weak robustness and feature extraction of cultural relic images, the attention mechanism is proposed to improve ResNet. Therefore, this network can pay attention to the key of feature areas in the image. Finally, the aforementioned methods are verified by experiments. The results show that compared with the YOLOv3 and other algorithms, the accuracy of the improved ResNet proposed in this experiment is above 90%. Furthermore, the number of missed and erroneous detection is the lowest, which are 171 and 134, respectively. The identified mAP indicator accuracy can reach 86%, which also exceeds SVD-Net and DenseNet. It can be seen that the constructed method can effectively detect and recognize the museum cultural relic images.

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“…Machine learning (ML) methods can be found in science, technology, health care, manufacturing, education, policing, and marketing [21]. DL is a sub-type of ML that works on deep neural network data representations in a supervised, semi-supervised, or unsupervised way [22,23]. DL techniques are used for the execution of different medical imaging-related tasks [24,25].…”

Section: Background and Literature Reviewmentioning

confidence: 99%

A Self-Activated CNN Approach for Multi-Class Chest-Related COVID-19 Detection

et al. 2021

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Chest diseases can be dangerous and deadly. They include many chest infections such as pneumonia, asthma, edema, and, lately, COVID-19. COVID-19 has many similar symptoms compared to pneumonia, such as breathing hardness and chest burden. However, it is a challenging task to differentiate COVID-19 from other chest diseases. Several related studies proposed a computer-aided COVID-19 detection system for the single-class COVID-19 detection, which may be misleading due to similar symptoms of other chest diseases. This paper proposes a framework for the detection of 15 types of chest diseases, including the COVID-19 disease, via a chest X-ray modality. Two-way classification is performed in proposed Framework. First, a deep learning-based convolutional neural network (CNN) architecture with a soft-max classifier is proposed. Second, transfer learning is applied using fully-connected layer of proposed CNN that extracted deep features. The deep features are fed to the classical Machine Learning (ML) classification methods. However, the proposed framework improves the accuracy for COVID-19 detection and increases the predictability rates for other chest diseases. The experimental results show that the proposed framework, when compared to other state-of-the-art models for diagnosing COVID-19 and other chest diseases, is more robust, and the results are promising.

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Explainable deep learning for efficient and robust pattern recognition: A survey of recent developments

Cited by 194 publications

References 36 publications

Research on the Privacy Security of Face Recognition Technology

Research on the Privacy Security of Face Recognition Technology

Museum Relic Image Detection and Recognition Based on Deep Learning

A Self-Activated CNN Approach for Multi-Class Chest-Related COVID-19 Detection

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