scholarly journals Multi-Source Deep Transfer Neural Network Algorithm

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3992 ◽  
Author(s):  
Jingmei Li ◽  
Weifei Wu ◽  
Di Xue ◽  
Peng Gao
Keyword(s):  
Neural Network ◽  
Probability Distribution ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Classification Performance ◽  
Classification Error ◽  
Target Domain ◽  
Source Domain ◽  
Network Algorithm ◽  
Neural Network Algorithm

Transfer learning can enhance classification performance of a target domain with insufficient training data by utilizing knowledge relating to the target domain from source domain. Nowadays, it is common to see two or more source domains available for knowledge transfer, which can improve performance of learning tasks in the target domain. However, the classification performance of the target domain decreases due to mismatching of probability distribution. Recent studies have shown that deep learning can build deep structures by extracting more effective features to resist the mismatching. In this paper, we propose a new multi-source deep transfer neural network algorithm, MultiDTNN, based on convolutional neural network and multi-source transfer learning. In MultiDTNN, joint probability distribution adaptation (JPDA) is used for reducing the mismatching between source and target domains to enhance features transferability of the source domain in deep neural networks. Then, the convolutional neural network is trained by utilizing the datasets of each source and target domain to obtain a set of classifiers. Finally, the designed selection strategy selects classifier with the smallest classification error on the target domain from the set to assemble the MultiDTNN framework. The effectiveness of the proposed MultiDTNN is verified by comparing it with other state-of-the-art deep transfer learning on three datasets.

Classification of Batik Authenticity Using Convolutional Neural Network Algorithm with Transfer Learning Method

2021 ◽  
Author(s):  
Farrel Athaillah Putra ◽  
Dwi Anggun Cahyati Jamil ◽  
Briliantino Abhista Prabandanu ◽  
Suhaili Faruq ◽  
Firsta Adi Pradana ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Learning Method ◽  
Network Algorithm ◽  
Neural Network Algorithm

scholarly journals Convolutional Neural Network Coupled with a Transfer-Learning Approach for Time-Series Flood Predictions

Water ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 96 ◽  
Author(s):  
Nobuaki Kimura ◽  
Ikuo Yoshinaga ◽  
Kenji Sekijima ◽  
Issaku Azechi ◽  
Daichi Baba
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Learning Approach ◽  
Target Domain ◽  
Training Time ◽  
Source Domain ◽  
Flood Model

East Asian regions in the North Pacific have recently experienced severe riverine flood disasters. State-of-the-art neural networks are currently utilized as a quick-response flood model. Neural networks typically require ample time in the training process because of the use of numerous datasets. To reduce the computational costs, we introduced a transfer-learning approach to a neural-network-based flood model. For a concept of transfer leaning, once the model is pretrained in a source domain with large datasets, it can be reused in other target domains. After retraining parts of the model with the target domain datasets, the training time can be reduced due to reuse. A convolutional neural network (CNN) was employed because the CNN with transfer learning has numerous successful applications in two-dimensional image classification. However, our flood model predicts time-series variables (e.g., water level). The CNN with transfer learning requires a conversion tool from time-series datasets to image datasets in preprocessing. First, the CNN time-series classification was verified in the source domain with less than 10% errors for the variation in water level. Second, the CNN with transfer learning in the target domain efficiently reduced the training time by 1/5 of and a mean error difference by 15% of those obtained by the CNN without transfer learning, respectively. Our method can provide another novel flood model in addition to physical-based models.

scholarly journals Cross-Domain Text Sentiment Analysis Based on CNN_FT Method

Information ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 162 ◽  
Author(s):  
Jiana Meng ◽  
Yingchun Long ◽  
Yuhai Yu ◽  
Dandan Zhao ◽  
Shuang Liu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Target Domain ◽  
Data Set ◽  
Source Domain ◽  
Cross Domain ◽  
The Cross ◽  
Fully Connected ◽  
Text Sentiment Analysis

Transfer learning is one of the popular methods for solving the problem that the models built on the source domain cannot be directly applied to the target domain in the cross-domain sentiment classification. This paper proposes a transfer learning method based on the multi-layer convolutional neural network (CNN). Interestingly, we construct a convolutional neural network model to extract features from the source domain and share the weights in the convolutional layer and the pooling layer between the source and target domain samples. Next, we fine-tune the weights in the last layer, named the fully connected layer, and transfer the models from the source domain to the target domain. Comparing with the classical transfer learning methods, the method proposed in this paper does not need to retrain the network for the target domain. The experimental evaluation of the cross-domain data set shows that the proposed method achieves a relatively good performance.

Automated detection of pneumonia cases using deep transfer learning with paediatric chest X-ray images

2021 ◽  
pp. 20201263
Author(s):  
Mohammad Salehi ◽  
Reza Mohammadi ◽  
Hamed Ghaffari ◽  
Nahid Sadighi ◽  
Reza Reiazi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Area Under Curve ◽  
Binary Classification ◽  
Classification Performance ◽  
Learning Approach ◽  
Insufficient Data ◽  
X Ray ◽  
Chest X Ray

Objective: Pneumonia is a lung infection and causes the inflammation of the small air sacs (Alveoli) in one or both lungs. Proper and faster diagnosis of pneumonia at an early stage is imperative for optimal patient care. Currently, chest X-ray is considered as the best imaging modality for diagnosing pneumonia. However, the interpretation of chest X-ray images is challenging. To this end, we aimed to use an automated convolutional neural network-based transfer-learning approach to detect pneumonia in paediatric chest radiographs. Methods: Herein, an automated convolutional neural network-based transfer-learning approach using four different pre-trained models (i.e. VGG19, DenseNet121, Xception, and ResNet50) was applied to detect pneumonia in children (1–5 years) chest X-ray images. The performance of different proposed models for testing data set was evaluated using five performances metrics, including accuracy, sensitivity/recall, Precision, area under curve, and F1 score. Results: All proposed models provide accuracy greater than 83.0% for binary classification. The pre-trained DenseNet121 model provides the highest classification performance of automated pneumonia classification with 86.8% accuracy, followed by Xception model with an accuracy of 86.0%. The sensitivity of the proposed models was greater than 91.0%. The Xception and DenseNet121 models achieve the highest classification performance with F1-score greater than 89.0%. The plotted area under curve of receiver operating characteristics of VGG19, Xception, ResNet50, and DenseNet121 models are 0.78, 0.81, 0.81, and 0.86, respectively. Conclusion: Our data showed that the proposed models achieve a high accuracy for binary classification. Transfer learning was used to accelerate training of the proposed models and resolve the problem associated with insufficient data. We hope that these proposed models can help radiologists for a quick diagnosis of pneumonia at radiology departments. Moreover, our proposed models may be useful to detect other chest-related diseases such as novel Coronavirus 2019. Advances in knowledge: Herein, we used transfer learning as a machine learning approach to accelerate training of the proposed models and resolve the problem associated with insufficient data. Our proposed models achieved accuracy greater than 83.0% for binary classification.

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