End-to-End Visual Domain Adaptation Network for Cross-Domain 3D CPS Data Retrieval

Liu, An-An; Xiang, Shijun; Nie, Weizhi; Song, Dan

doi:10.1109/access.2019.2937377

Cited by 5 publications

(5 citation statements)

References 31 publications

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“…So far, this is a preliminary dataset, which is not well balanced as Gualepis occupies a large proportion (888) of our dataset, and Acanthodii indet is least in number (34). We proceeded with an FT strategy to adjust the VGG16 model for our dataset [57]. The classification results showed that the accuracy rate satisfied the preliminary requirements of paleontologists.…”

Section: Discussionmentioning

confidence: 95%

ADMorph: A 3D Digital Microfossil Morphology Dataset for Deep Learning

et al. 2020

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Microfossils, tiny fossils whose study requires the use of a microscope, have been widely applied in many fields of earth, life, and environmental sciences. The abundance and high diversity of microfossils, as well as the need for rapid identification, call for automated methods to classify microfossils. In this study, we constructed an open dataset of three-dimensional (3D) microfossils and proposed a deep learning-based approach for microfossil classification. The dataset, named 'Archives of Digital Morphology' (ADMorph), currently contains more than ten thousand 3D models from five classes of 410 million-year-old fishes. The deep learning-based method includes data preprocessing, feature extraction, and 3D microfossil model classification. To assess the method performance and dataset representability, we performed extensive experiments. Compared with multiview convolutional neural networks (MVCNN) (91.54%), PointNet (64.13%), and VoxNet (78.15%), the method proposed herein had higher accuracy (97.60%) on the experimental dataset. We also verified data preprocessing (92.36%) and feature extraction (97.10%). We combined them to obtain the macroaveraging accuracy of 97.60%, the highest accuracy of 100%, and the lowest accuracy of 88.78%. We suggest that the proposed method can be applied to other 3D fossils and biomorphological research fields. The fast-accumulating 3D fossil models might become a source of information-rich datasets for deep learning.INDEX TERMS Archives of Digital Morphology, data preprocessing, feature extraction, 3D microfossil model classification, deep learning.

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Section: Discussionmentioning

confidence: 95%

ADMorph: A 3D Digital Microfossil Morphology Dataset for Deep Learning

et al. 2020

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“…• SCFN is superior to other representative view-based methods. Previous methods [8], [17] usually employ the pooling strategy to fuse multi-view features to obtain a 4 https://pytorch.org/ unified 3D model representation. Although this operation is robust to view input order, the pooling strategy is too simple to capture the multi-view spatial context information.…”

Section: A Comparison With State-of-the-art Methodsmentioning

confidence: 99%

“…In our experiments, the learning rate is fixed at 0.0001. Our method is implemented based on the PyTorch framework 4 . All experiments are conducted on a server with two GeForce GTX1080 GPUs equipped with 12G memory, one Intel (R) Xeon (R) CPU, and 32G RAM.…”

Section: Implementation Detailsmentioning

confidence: 99%

“…In recent years, with the wide application of 3D technology in virtual reality, 3D printing, medical diagnosis, and other fields [1]- [4], the number of 3D models is proliferating, which makes the 3D model classification and retrieval tasks receive a surge of attention. The most critical step in these tasks is to learn a discriminative 3D model descriptor.…”

Section: Introductionmentioning

confidence: 99%

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Semantic and Context Information Fusion Network for View-Based 3D Model Classification and Retrieval

et al. 2020

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In recent years, with the rapid development of 3D technology, view-based methods have shown excellent performance in both 3D model classification and retrieval tasks. In view-based methods, how to aggregate multi-view features is a key issue. There are two commonly used solutions in the existing methods: 1) Use pooling strategy to merge multi-view features, but it ignores the context information contained in the continuous view sequence. 2) Leverage grouping strategy or long short term memory networks (LSTM) to select representative views of the 3D model, however, it easily neglects the semantic information of individual views. In this paper, we propose a novel Semantic and Context information Fusion Network (SCFN) to compensate for these drawbacks. First, we render views from multiple perspectives of the 3D model and extract the raw feature of the individual view by 2D convolutional neural networks (CNN). Then we design the channel attention mechanism (CAM) to exploit the view-wise semantic information. By modeling the correlation among view feature channels, we can assign higher weights to useful feature attributes, while suppressing the useless. Next, we propose a context information fusion module (CFM) to fuse multiple view features to obtain a compact 3D representation. Extensive experiments are conducted on three popular datasets, i.e., ModelNet10, ModelNet40, and ShapeNetCore55, which can demonstrate the superiority of the proposed method comparing to the state-of-the-arts on both 3D classification and retrieval tasks.

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“…To address this challenge, the main research on domain adaptation techniques focuses on how a machine learning model built in a source domain can be adapted in a different but related target domain, which is necessary to avoid reconstruction efforts. In the field of knowledge engineering, many beneficial and promising examples with domain adaptation have been found, including image classification, object recognition, natural language processing, and feature learning [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Deep Domain Adaptation Model for Bearing Fault Diagnosis with Domain Alignment and Discriminative Feature Learning

Liu

2020

Shock and Vibration

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Deep learning techniques have been widely used to achieve promising results for fault diagnosis. In many real-world fault diagnosis applications, labeled training data (source domain) and unlabeled test data (target domain) have different distributions due to the frequent changes of working conditions, leading to performance degradation. This study proposes an end-to-end unsupervised domain adaptation bearing fault diagnosis model that combines domain alignment and discriminative feature learning on the basis of a 1D convolutional neural network. Joint training with classification loss, center-based discriminative loss, and correlation alignment loss between the two domains can adapt learned representations in the source domain for application to the target domain. Such joint training can also guarantee domain-invariant features with good intraclass compactness and interclass separability. Meanwhile, the extracted features can efficiently improve the cross-domain testing performance. Experimental results on the Case Western Reserve University bearing datasets confirm the superiority of the proposed method over many existing methods.

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End-to-End Visual Domain Adaptation Network for Cross-Domain 3D CPS Data Retrieval

Cited by 5 publications

References 31 publications

ADMorph: A 3D Digital Microfossil Morphology Dataset for Deep Learning

ADMorph: A 3D Digital Microfossil Morphology Dataset for Deep Learning

Semantic and Context Information Fusion Network for View-Based 3D Model Classification and Retrieval

Deep Domain Adaptation Model for Bearing Fault Diagnosis with Domain Alignment and Discriminative Feature Learning

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