Landslide Detection from Open Satellite Imagery Using Distant Domain Transfer Learning

Using convolutional neural network (CNN) methods and satellite images for landslide identification and classification is a very efficient and popular task in geological hazard investigations. However, traditional CNNs have two disadvantages: (1) insufficient training images from the study area and (2) uneven distribution of the training set and validation set. In this paper, we introduced distant domain transfer learning (DDTL) methods for landslide detection and classification. We first introduce scene classification satellite imagery into the landslide detection task. In addition, in order to more effectively extract information from satellite images, we innovatively add an attention mechanism to DDTL (AM-DDTL). In this paper, the Longgang study area, a district in Shenzhen City, Guangdong Province, has only 177 samples as the landslide target domain. We examine the effect of DDTL by comparing three methods: the convolutional CNN, pretrained model and DDTL. We compare different attention mechanisms based on the DDTL. The experimental results show that the DDTL method has better detection performance than the normal CNN, and the AM-DDTL models achieve 94% classification accuracy, which is 7% higher than the conventional DDTL method. The requirements for the detection and classification of potential landslides at different disaster zones can be met by applying the AM-DDTL algorithm, which outperforms traditional CNN methods.

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Transfer Learning with Convolutional Neural Networks for Diabetic Retinopathy Image Classification. A Review

Applied Sciences ◽

10.3390/app10062021 ◽

2020 ◽

Vol 10 (6) ◽

pp. 2021 ◽

Cited By ~ 1

Author(s):

Ibrahem Kandel ◽

Mauro Castelli

Keyword(s):

Diabetic Retinopathy ◽

Deep Learning ◽

Transfer Learning ◽

Main Idea ◽

Diabetic Patients ◽

Domain Transfer ◽

Domain Transfer Learning ◽

Enormous Number ◽

Deep Learning Model

Diabetic retinopathy (DR) is a dangerous eye condition that affects diabetic patients. Without early detection, it can affect the retina and may eventually cause permanent blindness. The early diagnosis of DR is crucial for its treatment. However, the diagnosis of DR is a very difficult process that requires an experienced ophthalmologist. A breakthrough in the field of artificial intelligence called deep learning can help in giving the ophthalmologist a second opinion regarding the classification of the DR by using an autonomous classifier. To accurately train a deep learning model to classify DR, an enormous number of images is required, and this is an important limitation in the DR domain. Transfer learning is a technique that can help in overcoming the scarcity of images. The main idea that is exploited by transfer learning is that a deep learning architecture, previously trained on non-medical images, can be fine-tuned to suit the DR dataset. This paper reviews research papers that focus on DR classification by using transfer learning to present the best existing methods to address this problem. This review can help future researchers to find out existing transfer learning methods to address the DR classification task and to show their differences in terms of performance.

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Deep Transfer Learning for Biology Cross-Domain Image Classification

Journal of Control Science and Engineering ◽

10.1155/2021/2518837 ◽

2021 ◽

Vol 2021 ◽

pp. 1-19

Author(s):

Chunfeng Guo ◽

Bin Wei ◽

Kun Yu

Keyword(s):

Image Classification ◽

Transfer Learning ◽

Poor Performance ◽

Target Domain ◽

Deep Convolutional Neural Networks ◽

Specific Domain ◽

Source Domain ◽

Cross Domain ◽

Domain Transfer ◽

Domain Transfer Learning

Automatic biology image classification is essential for biodiversity conservation and ecological study. Recently, due to the record-shattering performance, deep convolutional neural networks (DCNNs) have been used more often in biology image classification. However, training DCNNs requires a large amount of labeled data, which may be difficult to collect for some organisms. This study was carried out to exploit cross-domain transfer learning for DCNNs with limited data. According to the literature, previous studies mainly focus on transferring from ImageNet to a specific domain or transferring between two closely related domains. While this study explores deep transfer learning between species from different domains and analyzes the situation when there is a huge difference between the source domain and the target domain. Inspired by the analysis of previous studies, the effect of biology cross-domain image classification in transfer learning is proposed. In this work, the multiple transfer learning scheme is designed to exploit deep transfer learning on several biology image datasets from different domains. There may be a huge difference between the source domain and the target domain, causing poor performance on transfer learning. To address this problem, multistage transfer learning is proposed by introducing an intermediate domain. The experimental results show the effectiveness of cross-domain transfer learning and the importance of data amount and validate the potential of multistage transfer learning.

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Learning Domain-Independent Deep Representations by Mutual Information Minimization

Computational Intelligence and Neuroscience ◽

10.1155/2019/9414539 ◽

2019 ◽

Vol 2019 ◽

pp. 1-14 ◽

Cited By ~ 1

Author(s):

Ke Wang ◽

Jiayong Liu ◽

Jing-Yan Wang

Keyword(s):

Mutual Information ◽

Transfer Learning ◽

Representation Learning ◽

Target Domain ◽

Source Domain ◽

Cross Domain ◽

Data Points ◽

Data Point ◽

Domain Transfer ◽

Domain Transfer Learning

Domain transfer learning aims to learn common data representations from a source domain and a target domain so that the source domain data can help the classification of the target domain. Conventional transfer representation learning imposes the distributions of source and target domain representations to be similar, which heavily relies on the characterization of the distributions of domains and the distribution matching criteria. In this paper, we proposed a novel framework for domain transfer representation learning. Our motive is to make the learned representations of data points independent from the domains which they belong to. In other words, from an optimal cross-domain representation of a data point, it is difficult to tell which domain it is from. In this way, the learned representations can be generalized to different domains. To measure the dependency between the representations and the corresponding domain which the data points belong to, we propose to use the mutual information between the representations and the domain-belonging indicators. By minimizing such mutual information, we learn the representations which are independent from domains. We build a classwise deep convolutional network model as a representation model and maximize the margin of each data point of the corresponding class, which is defined over the intraclass and interclass neighborhood. To learn the parameters of the model, we construct a unified minimization problem where the margins are maximized while the representation-domain mutual information is minimized. In this way, we learn representations which are not only discriminate but also independent from domains. An iterative algorithm based on the Adam optimization method is proposed to solve the minimization to learn the classwise deep model parameters and the cross-domain representations simultaneously. Extensive experiments over benchmark datasets show its effectiveness and advantage over existing domain transfer learning methods.

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Document Image Classification with Intra-Domain Transfer Learning and Stacked Generalization of Deep Convolutional Neural Networks

2018 24th International Conference on Pattern Recognition (ICPR) ◽

10.1109/icpr.2018.8545630 ◽

2018 ◽

Cited By ~ 9

Author(s):

Arindam Das ◽

Saikat Roy ◽

Ujjwal Bhattacharya ◽

Swapan K. Parui

Keyword(s):

Neural Networks ◽

Image Classification ◽

Transfer Learning ◽

Convolutional Neural Networks ◽

Document Image ◽

Deep Convolutional Neural Networks ◽

Stacked Generalization ◽

Domain Transfer ◽

Domain Transfer Learning

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Cross-Domain Transfer Learning for Dependency Parsing

Natural Language Processing and Chinese Computing - Lecture Notes in Computer Science ◽

10.1007/978-3-030-32236-6_77 ◽

2019 ◽

pp. 835-844 ◽

Cited By ~ 1

Author(s):

Zuchao Li ◽

Junru Zhou ◽

Hai Zhao ◽

Rui Wang

Keyword(s):

Transfer Learning ◽

Dependency Parsing ◽

Cross Domain ◽

Domain Transfer ◽

Domain Transfer Learning

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AGE ESTIMATION USING SPECIFIC DOMAIN TRANSFER LEARNING

Jordanian Journal of Computers and Information Technology ◽

10.5455/jjcit.71-1571410322 ◽

2020 ◽

pp. 1

Author(s):

Arwa Shannaq ◽

Lamiaa Elrefaei

Keyword(s):

Transfer Learning ◽

Age Estimation ◽

Specific Domain ◽

Domain Transfer ◽

Domain Transfer Learning

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Tri-training for Dependency Parsing Domain Adaptation

ACM Transactions on Asian and Low-Resource Language Information Processing ◽

10.1145/3488367 ◽

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.

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

Electronics ◽

10.3390/electronics9030445 ◽

2020 ◽

Vol 9 (3) ◽

pp. 445 ◽

Cited By ~ 9

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.

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