Stable Acquisition of Fine-Grained Segments Using Batch Normalization and Focal Loss with L1 Regularization in U-Net Structure

Choi, Seoung-Ho; Jung, Sung Hoon

doi:10.5391/ijfis.2020.20.1.59

Cited by 9 publications

(4 citation statements)

References 14 publications

(30 reference statements)

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“…In order to make the results more intuitive, they are shown in Figures 17 and 18 The first attempt was to add batch normalization to the network [27]. The essence of the neural network learning process is to learn about data distribution [28]. Therefore, the introduction of batch normalization to normalize the data in the network can improve the network promotion ability and speed up the training speed to a certain extent.…”

Section: Further Optimizationmentioning

confidence: 99%

Ship Type Recognition Based on Ship Navigating Trajectory and Convolutional Neural Network

Yang

Wang

Liu

2022

JMSE

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With the aim to solve the problem of missing or tampering of ship type information in AIS information, in this paper, a novel ship type recognition scheme based on ship navigating trajectory and convolutional neural network (CNN) is proposed. Firstly, according to speed and acceleration of the ship, three ship navigating situations, i.e., static, normal navigation and maneuvering, are integrated into the process of trajectory images generation in the form of pixels. Then, three kinds of modular network structures with different depths are trained and optimized to determine the appropriate convolutional neural network structure. In the validation phase of the model, a large amount of verified data with a time span of one month was used, covering a variety of water conditions including open water, ports, rivers and lakes. Following this approach, a kind of CNN scheme which can be directly used to identify ship types in a wide range of waters is proposed. This scheme can be used to judge the ship type when the static information is completely missing and to test the data when the ship type information is partially missing.

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Section: Further Optimizationmentioning

confidence: 99%

Ship Type Recognition Based on Ship Navigating Trajectory and Convolutional Neural Network

Yang

Wang

Liu

2022

JMSE

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“…The network structure is based on U-Net with some modifications. Since blood vessels have distributed sizes, and the U-net structure is little sensitive to very high spatial frequency information [48], there is a deviation in the reconstruction of peripheral blood vessels. So we added the batch normalization (BN) [49] method after each down-sampling convolutional layer, to overcome this problem (as shown in Figure 2).…”

Section: B Network Architecturementioning

confidence: 99%

Deep Learning-Based Photoacoustic Imaging of Vascular Network Through Thick Porous Media

Gao

Xü

Chen

et al. 2022

IEEE Trans. Med. Imaging

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Photoacoustic imaging is a promising approach used to realize in vivo transcranial cerebral vascular imaging. However, the strong attenuation and distortion of the photoacoustic wave caused by the thick porous skull greatly affect the imaging quality. In this study, we developed a convolutional neural network based on U-Net to extract the effective photoacoustic information hidden in the speckle patterns obtained from vascular network images datasets under porous media. Our simulation and experimental results show that the proposed neural network can learn the mapping relationship between the speckle pattern and the target, and extract the photoacoustic signals of the vessels submerged in noise to reconstruct high-quality images of the vessels with a sharp outline and a clean background. Compared with the traditional photoacoustic reconstruction methods, the proposed deep learning-based reconstruction algorithm has a better performance with a lower mean absolute error, higher structural similarity, and higher peak signal-to-noise ratio of reconstructed images. In conclusion, the proposed neural network can effectively extract valid information from highly blurred speckle patterns for the rapid reconstruction of target images, which offers promising applications in transcranial photoacoustic imaging.

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“…Focal loss was proposed by He et al [21] in 2017. Focal loss takes the different level of training difficulty of samples into consideration and focuses more on the difficult-to-train samples; therefore, it has been applied in many fields, such as object detection, imbalanced data classification, and so on [22][23][24]. To identify classes with fewer training samples more accurately, a modified focal loss function is used to replace cross-entropy loss function in the proposed model.…”

Section: Related Workmentioning

confidence: 99%

A Residual Learning-Based Network Intrusion Detection System

Man

Sun

2021

Security and Communication Networks

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Neural networks have been proved to perform well in network intrusion detection. In order to acquire better features of network traffic, more learning layers are necessarily required. However, according to the results of the previous research, adding layers to the neural networks might fail to improve the classification results. In fact, after the number of layers has reached a certain threshold, performance of the model tends to degrade. In this paper, we propose a network intrusion detection model based on residual learning. After transforming the UNSW-NB15 data set into images, deeper convolutional neural networks with residual blocks are built to learn more critical features. Instead of the cross-entropy loss function, the modified focal loss is calculated to address the class imbalance problem in the training set and identify minor attacks in the testing set. Batch normalization and global average pooling are used to avoid overfitting and enhance the model. Experimental results show that the proposed model can improve attack detection accuracy compared with existing models.

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Stable Acquisition of Fine-Grained Segments Using Batch Normalization and Focal Loss with L1 Regularization in U-Net Structure

Cited by 9 publications

References 14 publications

Ship Type Recognition Based on Ship Navigating Trajectory and Convolutional Neural Network

Ship Type Recognition Based on Ship Navigating Trajectory and Convolutional Neural Network

Deep Learning-Based Photoacoustic Imaging of Vascular Network Through Thick Porous Media

A Residual Learning-Based Network Intrusion Detection System

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