A dual autoencoder employing separable convolutional layers for image denoising and deblurring is represented. Combining two autoencoders is presented to gain higher accuracy and simultaneously reduce the complexity o...
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A dual autoencoder employing separable convolutional layers for image denoising and deblurring is represented. Combining two autoencoders is presented to gain higher accuracy and simultaneously reduce the complexity of neural network parameters by using separable convolutional layers. In the proposed structure of the dual autoencoder, the first autoencoder aims to denoise the image, while the second one aims to enhance the quality of the denoised image. The research includes Gaussian noise (Gaussian blur), Poisson noise, speckle noise, and random impulse noise. The advantages of the proposed neural network are the number reduction in the trainable parameters and the increase in the similarity between the denoised or deblurred image and the original one. The similarity is increased by decreasing the main square error and increasing the structural similarity index. The advantages of a dual autoencoder network with separable convolutional layers are demonstrated by a comparison of the proposed network with a convolutional autoencoder and dual convolutional autoencoder.
In robot-assisted minimally invasive surgery (RMIS), robotic palpation is vital for enhancing tissue assessment accuracy, especially for tumor depth detection, which is crucial for precise resections and improved trea...
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In robot-assisted minimally invasive surgery (RMIS), robotic palpation is vital for enhancing tissue assessment accuracy, especially for tumor depth detection, which is crucial for precise resections and improved treatment outcomes. By complementing and leveraging the strengths of data from multiple tactile sensors, the tumor detection task can realize significant improvements in accuracy and reliability, thereby enhancing overall performance in robotic palpation in RMIS. However, challenges arise due to differences in sensor modalities and the lack of unified data representation. To address this, we fabricate silicone-based phantom tissue to simulate soft tissue and embedded simulated tumors with hard inclusions at depths ranging from 0-11 mm, treating it as a 12-class classification problem. We conduct two robotic palpation experiments: one with the BarrettHand capacitive sensor and the other with the Digit sensor, collecting two tactile datasets. To explore the joint learnability of datasets collected from different tactile sensors, we design a dual autoencoder-based joint learning framework that integrates two recurrent autoencoders to process the two different tactile datasets. By applying a joint loss mechanism to connect their latent spaces, the autoencoders are trained jointly together, with the latent representation used for supervised classification. Extensive experiments show that joint learning enables sharing of features learned from different tactile datasets, thereby enhancing learning efficiency and classification accuracy for tactile datasets with different modalities and improving the performance of both autoencoders compared to independent training.
This paper presents a dual autoencoder model for abnormality detection from retinal fundus images. In this paper, the dual autoencoder along with (Kernal Density Estimation) KDE is used to improve the result as compar...
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In recent years, deep neural networks have been widely used in recommender systems. Neural collaborative filtering is a popular work to model complex interactions between users and items with deep learning. However, m...
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ISBN:
(数字)9781728186719
ISBN:
(纸本)9781728186719
In recent years, deep neural networks have been widely used in recommender systems. Neural collaborative filtering is a popular work to model complex interactions between users and items with deep learning. However, methods that are based on collaborative filtering usually focus on learning embedding with the factorization of pairwise interactions, thereby causing embedding to be insufficient in capturing the complex relationships between users and items. To alleviate the above problem, in this paper, we propose a novel recommendation method based on collaborative filtering with dual autoencoder (CFDA). In the proposed method, we use dual autoencoder to learn hidden representations of users and items simultaneously, and we minimize the deviation of the training data by learning the user and item representations. Extensive experiments on several datasets demonstrate that the proposed method outperforms the baseline methods that are based on neural collaborative filtering.
Multi-label classification aims to deal with the problem that an object may be associated with one or more labels, which is a more difficult task due to the complex nature of multi-label data. The crucial problem of m...
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Multi-label classification aims to deal with the problem that an object may be associated with one or more labels, which is a more difficult task due to the complex nature of multi-label data. The crucial problem of multi-label classification is the more robust and higher-level feature representation learning, which can reduce non-helpful feature attributes from the input space prior to training. In recent years, deep learning methods based on autoencoders have achieved excellent performance in multi-label classification for the advantages of powerful representations learning ability and fast convergence speed. However, most existing autoencoder-based methods only rely on the single autoencoder model, which pose challenges for multi-label feature representations learning and fail to measure similarities between data spaces. To address this problem, in this paper, we propose a novel representation learning method with dual autoencoder for multi-label classification. Compared to the existing autoencoder-based methods, our proposed method can capture different characteristics and more abstract features from data by the serially connection of two different types of autoencoders. More specifically, firstly, the algorithm of Reconstruction Independent Component Analysis (RICA) in sparse autoencoder is trained on patches on all training and test dataset for robust global feature representations learning. Secondly, with the output of RICA, stacked autoencoder with manifold regularization (SAMR) is introduced to ameliorate the quality of multi-label features learning. Comprehensive experiments on several real-world data sets demonstrate the effectiveness of our proposed approach compared with several competing state-of-the-art methods.
Anomaly detection on attributed networks aims at finding nodes whose patterns deviate significantly from the majority of reference nodes, which is pervasive in many applications such as network intrusion detection and...
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ISBN:
(纸本)9781509066315
Anomaly detection on attributed networks aims at finding nodes whose patterns deviate significantly from the majority of reference nodes, which is pervasive in many applications such as network intrusion detection and social spammer detection. However, most existing methods neglect the complex cross-modality interactions between network structure and node attribute. In this paper, we propose a deep joint representation learning framework for anomaly detection through a dual autoencoder (AnomalyDAE), which captures the complex interactions between network structure and node attribute for high-quality embeddings. Specifically, AnomalyDAE consists of a structure autoencoder and an attribute autoencoder to learn both node embedding and attribute embedding jointly in latent space. Moreover, attention mechanism is employed in structure encoder to learn the importance between a node and its neighbors for an effective capturing of structure pattern, which is important to anomaly detection. Besides, by taking both the node embedding and attribute embedding as inputs of attribute decoder, the cross-modality interactions between network structure and node attribute are learned during the reconstruction of node attribute. Finally, anomalies can be detected by measuring the reconstruction errors of nodes from both the structure and attribute perspectives. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed method.
Volcanic seismicity is one of the most relevant parameters for the evaluation of volcanic activity and consequently the prognosis of eruptions. Earthquakes of volcanic origin are of different classes, directly related...
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Volcanic seismicity is one of the most relevant parameters for the evaluation of volcanic activity and consequently the prognosis of eruptions. Earthquakes of volcanic origin are of different classes, directly related to the physical process that generates them. The distribution of the data between classes of seismic-volcanic signals generally presents an unbalanced profile (imbalanced datasets), which can hinder the performance of the classification in machine learning models. Therefore, this research presents a characterization technique (feature extract) that, in addition to reducing the dimension of each seismic record, allows a representation of the signals with the most relevant and significant information. This work proposes the use of a dual autoencoder feature, which is compared with conventional characterization techniques, such as linear prediction coefficients and principal component analysis. The training of the model was performed with a dataset containing volcano-tectonic (VT) earthquakes, long period events, and Tornillo-type events of the Galeras volcano, one of the most active volcanoes in Colombia. The classification results reach 99% of the classification of the mentioned classes.
Mineral prospectivity mapping (MPM) through the identification of prospective areas, by analyzing various exploration data and integrating them, plays a crucial role in reducing risk and improving decision-making in m...
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Mineral prospectivity mapping (MPM) through the identification of prospective areas, by analyzing various exploration data and integrating them, plays a crucial role in reducing risk and improving decision-making in mineral exploration. However, this process is complex and faces many challenges due to the uncertainties inherent in the data and the various models used. In this study, our aim is to integrate several advanced methods to identify anomalies and use a new method to evaluate the performance of the developed models. To this end, five deep learning algorithms were employed for MPM, and their results were combined using a new method based on Bayesian statistics. This method was applied to five different models, resulting in a final composite model with a high level of confidence. The evaluation of results was performed using the Prediction Area plot (P-A plot). The final model demonstrated 6% higher accuracy compared to individual models and identified a smaller area as high-potential regions. Geologically, the results of the final model showed good alignment with microgranite, granodiorite to diorite, quartz diorite, and quartz monzodiorite units, indicating the success of this method in forward-looking modeling. The findings of this research suggest that combining models using this index can help reduce uncertainty and improve predictions in the identification of exploration targets, leading to more accurate decision-making and reduced exploration risks. This approach can be effectively applied in future exploration efforts.
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