scholarly journals Deep adversarial domain adaptation network

2020 ◽  
Vol 17 (5) ◽  
pp. 172988142096464
Author(s):  
Lan Wu ◽  
Chongyang Li ◽  
Qiliang Chen ◽  
Binquan Li
Keyword(s):  
Transfer Learning ◽  
Loss Function ◽  
Classification Accuracy ◽  
Domain Adaptation ◽  
Recognition Accuracy ◽  
Data Sets ◽  
Maximum Mean Discrepancy ◽  
Consistent Feature ◽  
Simulation Results ◽  
Domain Transfer

The advantage of adversarial domain adaptation is that it uses the idea of adversarial adaptation to confuse the feature distribution of two domains and solve the problem of domain transfer in transfer learning. However, although the discriminator completely confuses the two domains, adversarial domain adaptation still cannot guarantee the consistent feature distribution of the two domains, which may further deteriorate the recognition accuracy. Therefore, in this article, we propose a deep adversarial domain adaptation network, which optimises the feature distribution of the two confused domains by adding multi-kernel maximum mean discrepancy to the feature layer and designing a new loss function to ensure good recognition accuracy. In the last part, some simulation results based on the Office-31 and Underwater data sets show that the deep adversarial domain adaptation network can optimise the feature distribution and promote positive transfer, thus improving the classification accuracy.

scholarly journals Classification of jujube defects in small data sets based on transfer learning

2021 ◽  
Author(s):  
Jianping Ju ◽  
Hong Zheng ◽  
Xiaohang Xu ◽  
Zhongyuan Guo ◽  
Zhaohui Zheng ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Loss Function ◽  
Training Model ◽  
Parameter Distribution ◽  
Test Accuracy ◽  
Small Data ◽  
Data Sets ◽  
Data Set ◽  
Small Data Sets

AbstractAlthough convolutional neural networks have achieved success in the field of image classification, there are still challenges in the field of agricultural product quality sorting such as machine vision-based jujube defects detection. The performance of jujube defect detection mainly depends on the feature extraction and the classifier used. Due to the diversity of the jujube materials and the variability of the testing environment, the traditional method of manually extracting the features often fails to meet the requirements of practical application. In this paper, a jujube sorting model in small data sets based on convolutional neural network and transfer learning is proposed to meet the actual demand of jujube defects detection. Firstly, the original images collected from the actual jujube sorting production line were pre-processed, and the data were augmented to establish a data set of five categories of jujube defects. The original CNN model is then improved by embedding the SE module and using the triplet loss function and the center loss function to replace the softmax loss function. Finally, the depth pre-training model on the ImageNet image data set was used to conduct training on the jujube defects data set, so that the parameters of the pre-training model could fit the parameter distribution of the jujube defects image, and the parameter distribution was transferred to the jujube defects data set to complete the transfer of the model and realize the detection and classification of the jujube defects. The classification results are visualized by heatmap through the analysis of classification accuracy and confusion matrix compared with the comparison models. The experimental results show that the SE-ResNet50-CL model optimizes the fine-grained classification problem of jujube defect recognition, and the test accuracy reaches 94.15%. The model has good stability and high recognition accuracy in complex environments.

Tri-training for Dependency Parsing Domain Adaptation

2022 ◽  
Vol 21 (3) ◽  
pp. 1-17
Author(s):  
Shu Jiang ◽  
Zuchao Li ◽  
Hai Zhao ◽  
Bao-Liang Lu ◽  
Rui Wang
Keyword(s):  
Transfer Learning ◽  
High Performance ◽  
Domain Adaptation ◽  
Language Model ◽  
Training Methods ◽  
Dependency Parsing ◽  
Cross Domain ◽  
Cross Lingual ◽  
Domain Transfer ◽  
Domain Transfer Learning

In recent years, the research on dependency parsing focuses on improving the accuracy of the domain-specific (in-domain) test datasets and has made remarkable progress. However, there are innumerable scenarios in the real world that are not covered by the dataset, namely, the out-of-domain dataset. As a result, parsers that perform well on the in-domain data usually suffer from significant performance degradation on the out-of-domain data. Therefore, to adapt the existing in-domain parsers with high performance to a new domain scenario, cross-domain transfer learning methods are essential to solve the domain problem in parsing. This paper examines two scenarios for cross-domain transfer learning: semi-supervised and unsupervised cross-domain transfer learning. Specifically, we adopt a pre-trained language model BERT for training on the source domain (in-domain) data at the subword level and introduce self-training methods varied from tri-training for these two scenarios. The evaluation results on the NLPCC-2019 shared task and universal dependency parsing task indicate the effectiveness of the adopted approaches on cross-domain transfer learning and show the potential of self-learning to cross-lingual transfer learning.

scholarly journals Optimizing the Performance of Breast Cancer Classification by Employing the Same Domain Transfer Learning from Hybrid Deep Convolutional Neural Network Model

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 445 ◽  
Author(s):  
Laith Alzubaidi ◽  
Omran Al-Shamma ◽  
Mohammed A. Fadhel ◽  
Laith Farhan ◽  
Jinglan Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Transfer Learning ◽  
Cancer Diagnosis ◽  
Classification Accuracy ◽  
Cancer Classification ◽  
Learning Models ◽  
Proposed Model ◽  
Domain Transfer ◽  
Domain Transfer Learning

Breast cancer is a significant factor in female mortality. An early cancer diagnosis leads to a reduction in the breast cancer death rate. With the help of a computer-aided diagnosis system, the efficiency increased, and the cost was reduced for the cancer diagnosis. Traditional breast cancer classification techniques are based on handcrafted features techniques, and their performance relies upon the chosen features. They also are very sensitive to different sizes and complex shapes. However, histopathological breast cancer images are very complex in shape. Currently, deep learning models have become an alternative solution for diagnosis, and have overcome the drawbacks of classical classification techniques. Although deep learning has performed well in various tasks of computer vision and pattern recognition, it still has some challenges. One of the main challenges is the lack of training data. To address this challenge and optimize the performance, we have utilized a transfer learning technique which is where the deep learning models train on a task, and then fine-tune the models for another task. We have employed transfer learning in two ways: Training our proposed model first on the same domain dataset, then on the target dataset, and training our model on a different domain dataset, then on the target dataset. We have empirically proven that the same domain transfer learning optimized the performance. Our hybrid model of parallel convolutional layers and residual links is utilized to classify hematoxylin–eosin-stained breast biopsy images into four classes: invasive carcinoma, in-situ carcinoma, benign tumor and normal tissue. To reduce the effect of overfitting, we have augmented the images with different image processing techniques. The proposed model achieved state-of-the-art performance, and it outperformed the latest methods by achieving a patch-wise classification accuracy of 90.5%, and an image-wise classification accuracy of 97.4% on the validation set. Moreover, we have achieved an image-wise classification accuracy of 96.1% on the test set of the microscopy ICIAR-2018 dataset.

scholarly journals Deep transfer learning for aortic root dilation identification in 3D ultrasound images

2018 ◽  
Vol 4 (1) ◽  
pp. 71-74 ◽  
Author(s):  
Jannis Hagenah ◽  
Mattias Heinrich ◽  
Floris Ernst
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Aortic Root ◽  
Classification Accuracy ◽  
3D Ultrasound ◽  
Small Data ◽  
Data Sets ◽  
Ultrasound Images ◽  
Small Data Sets ◽  
Learned Features

AbstractPre-operative planning of valve-sparing aortic root reconstruction relies on the automatic discrimination of healthy and pathologically dilated aortic roots. The basis of this classification are features extracted from 3D ultrasound images. In previously published approaches, handcrafted features showed a limited classification accuracy. However, feature learning is insufficient due to the small data sets available for this specific problem. In this work, we propose transfer learning to use deep learning on these small data sets. For this purpose, we used the convolutional layers of the pretrained deep neural network VGG16 as a feature extractor. To simplify the problem, we only took two prominent horizontal slices throgh the aortic root, the coaptation plane and the commissure plane, into account by stitching the features of both images together and training a Random Forest classifier on the resulting feature vectors. We evaluated this method on a data set of 48 images (24 healthy, 24 dilated) using 10-fold cross validation. Using the deep learned features we could reach a classification accuracy of 84 %, which clearly outperformed the handcrafted features (71 % accuracy). Even though the VGG16 network was trained on RGB photos and for different classification tasks, the learned features are still relevant for ultrasound image analysis of aortic root pathology identification. Hence, transfer learning makes deep learning possible even on very small ultrasound data sets.

scholarly journals Wheat Ear Recognition Based on RetinaNet and Transfer Learning

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4845
Author(s):  
Jingbo Li ◽  
Changchun Li ◽  
Shuaipeng Fei ◽  
Chunyan Ma ◽  
Weinan Chen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Transfer Learning ◽  
Recognition Accuracy ◽  
Image Data ◽  
Growth Stages ◽  
Data Sets ◽  
Yield Estimation ◽  
Ear Recognition ◽  
Average Accuracy ◽  
Different Growth Stages

The number of wheat ears is an essential indicator for wheat production and yield estimation, but accurately obtaining wheat ears requires expensive manual cost and labor time. Meanwhile, the characteristics of wheat ears provide less information, and the color is consistent with the background, which can be challenging to obtain the number of wheat ears required. In this paper, the performance of Faster regions with convolutional neural networks (Faster R-CNN) and RetinaNet to predict the number of wheat ears for wheat at different growth stages under different conditions is investigated. The results show that using the Global WHEAT dataset for recognition, the RetinaNet method, and the Faster R-CNN method achieve an average accuracy of 0.82 and 0.72, with the RetinaNet method obtaining the highest recognition accuracy. Secondly, using the collected image data for recognition, the R2 of RetinaNet and Faster R-CNN after transfer learning is 0.9722 and 0.8702, respectively, indicating that the recognition accuracy of the RetinaNet method is higher on different data sets. We also tested wheat ears at both the filling and maturity stages; our proposed method has proven to be very robust (the R2 is above 90). This study provides technical support and a reference for automatic wheat ear recognition and yield estimation.

scholarly journals Sentiment Classification for Financial Texts Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Shanshan Dong ◽  
Chang Liu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Transfer Learning ◽  
Loss Function ◽  
Classification Accuracy ◽  
Sentiment Classification ◽  
Target Domain ◽  
Text Data ◽  
Source Domain ◽  
Cross Domain

Sentiment classification for financial texts is of great importance for predicting stock markets and financial crises. At present, with the popularity of applications in the field of natural language processing (NLP) adopting deep learning, the application of automatic text classification and text-based sentiment classification has become more and more extensive. However, in the field of financial text-based sentiment classification, due to a lack of labeled samples, such applications are limited. A domain-adaptation-based financial text sentiment classification method is proposed in this paper, which can adopt source domain (SD) text data with sentiment labels and a large amount of unlabeled target domain (TD) financial text data as training samples for the proposed neural network. The proposed method is a cross-domain transfer-learning-based method. The domain classification subnetwork is added to the original neural network, and the domain classification loss function is also added to the original training loss function. Therefore, the network can simultaneously adapt to the target domain and then accomplish the classification task. The experiment of the proposed sentiment classification transfer learning method is carried out through an open-source dataset. The proposed method in this paper uses the reviews of Amazon Books, DVDs, electronics, and kitchen appliances as the source domain for cross-domain learning, and the classification accuracy rates can reach 65.0%, 61.2%, 61.6%, and 66.3%, respectively. Compared with nontransfer learning, the classification accuracy rate has improved by 11.0%, 7.6%, 11.4%, and 13.4%, respectively.

scholarly journals Ontology-Driven Cross-Domain Transfer Learning

2020 ◽  
Author(s):  
Mattia Fumagalli ◽  
Gábor Bella ◽  
Samuele Conti ◽  
Fausto Giunchiglia
Keyword(s):  
Learning Community ◽  
Transfer Learning ◽  
Domain Adaptation ◽  
Classification Performance ◽  
Knowledge Domains ◽  
Learning Framework ◽  
Cross Domain ◽  
First Results ◽  
Domain Transfer ◽  
Domain Transfer Learning

The aim of transfer learning is to reuse learnt knowledge across different contexts. In the particular case of cross-domain transfer (also known as domain adaptation), reuse happens across different but related knowledge domains. While there have been promising first results in combining learning with symbolic knowledge to improve cross-domain transfer results, the singular ability of ontologies for providing classificatory knowledge has not been fully exploited so far by the machine learning community. We show that ontologies, if properly designed, are able to support transfer learning by improving generalization and discrimination across classes. We propose an architecture based on direct attribute prediction for combining ontologies with a transfer learning framework, as well as an ontology-based solution for cross-domain generalization based on the integration of top-level and domain ontologies. We validate the solution on an experiment over an image classification task, demonstrating the system’s improved classification performance.

scholarly journals Multiclassification of Endoscopic Colonoscopy Images Based on Deep Transfer Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yan Wang ◽  
Zixuan Feng ◽  
Liping Song ◽  
Xiangbin Liu ◽  
Shuai Liu
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Classification Accuracy ◽  
Dietary Habits ◽  
Recognition Rate ◽  
Learning Task ◽  
Colorectal Polyps ◽  
Data Sets ◽  
Fine Tune

With the continuous improvement of human living standards, dietary habits are constantly changing, which brings various bowel problems. Among them, the morbidity and mortality rates of colorectal cancer have maintained a significant upward trend. In recent years, the application of deep learning in the medical field has become increasingly spread aboard and deep. In a colonoscopy, Artificial Intelligence based on deep learning is mainly used to assist in the detection of colorectal polyps and the classification of colorectal lesions. But when it comes to classification, it can lead to confusion between polyps and other diseases. In order to accurately diagnose various diseases in the intestines and improve the classification accuracy of polyps, this work proposes a multiclassification method for medical colonoscopy images based on deep learning, which mainly classifies the four conditions of polyps, inflammation, tumor, and normal. In view of the relatively small number of data sets, the network firstly trained by transfer learning on ImageNet was used as the pretraining model, and the prior knowledge learned from the source domain learning task was applied to the classification task about intestinal illnesses. Then, we fine-tune the model to make it more suitable for the task of intestinal classification by our data sets. Finally, the model is applied to the multiclassification of medical colonoscopy images. Experimental results show that the method in this work can significantly improve the recognition rate of polyps while ensuring the classification accuracy of other categories, so as to assist the doctor in the diagnosis of surgical resection.

scholarly journals Enhancing Histopathological Colorectal Cancer Image Classification by using Convolutional Neural Network

2021 ◽  
Author(s):  
Radwan Al.Shawesh ◽  
Yi Xiang Chen
Keyword(s):  
Colorectal Cancer ◽  
Transfer Learning ◽  
Classification Accuracy ◽  
Validation Dataset ◽  
Data Sets ◽  
Classification Methods ◽  
Tumor Diseases ◽  
Histopathological Images ◽  
Death Causes ◽  
Better Than

AbstractColorectal cancer (CRC) also known as bowl cancer is one of the leading death causes worldwide. Early diagnosis has become vital for a successful treatment. Now days with the new advancements in Convolutional Neural networks (CNNs) it’s possible to classify different images of CRC into different classes. Today It is crucial for physician to take advantage of the new advancement’s in deep learning, since classification methods are becoming more and more accurate and efficient. In this study, we introduce a method to improve the classification accuracy from previous studies that used the National Center for Tumor diseases (NCT) data sets. We adapt the ResNet-50 model in our experiment to classify the CRC histopathological images. Furthermore, we utilize transfer learning and fine-tunning techniques to improve the accuracy. Our Experiment results show that ResNet_50 network is the best CNN architecture so far for classifying CRC histopathological images on the NCT Biobank open source dataset. In addition to that using transfer learning allow us to obtain 97.7% accuracy on the validation dataset, which is better than all previous results we found in literature.

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