Visualitza Institute of New Imaging Technologies (INIT) per paraule clau "deep learning"

Ara mostrant els elements 1-16 d 16

    • openAccess   A set of deep learning algorithms for air quality prediction applications 

      Iskandaryan, Ditsuhi; Ramos, Francisco; Trilles, Sergio Elsevier (2023)
      This paper presents a set of machine learning algorithms, including grid-based (Bidirectional Convolutional Long Short-Term Memory) and graph-based (Attention Temporal Graph Convolutional Network) algorithms to predict air ...

    • openAccess   A study of deep neural networks for human activity recognition 

      Sansano-Sansano, Emilio; Montoliu Colás, Raul; Belmonte-Fernández, Óscar Wiley (2020)
      Human activity recognition and deep learning are two fields that have attracted attention in recent years. The former due to its relevance in many application domains, such as ambient assisted living or health monitoring, ...

    • openAccess   A survey on uncertainty quantification in deep learning for financial time series prediction 

      Blasco, Txus; Sánchez Garreta, Josep Salvador; García, Vicente Elsevier Science Direct (2024-01-28)
      Investors make decisions about buying and selling a financial asset based on available information. The traditional approach in Deep Learning when trying to predict the behavior of an asset is to take a price history, train ...

    • closedAccess   Assessing similarities between spatial point patterns with a Siamese neural network discriminant model 

      Jalilian, Abdollah; Mateu, Jorge Springer (2023)
      Identifying structural differences among observed point patterns from several populations is of interest in several applications. We use deep convolutional neural networks and employ a Siamese framework to build a discriminant ...

    • openAccess   Deep Hashing Based on Class-Discriminated Neighborhood Embedding 

      kang, jian; Fernandez-Beltran, Ruben; Zhen, Ye; Tong, Xiaohua Institute of Electrical and Electronics Engineers (2020-09-30)
      Deep-hashing methods have drawn significant attention during the past years in the field of remote sensing (RS) owing to their prominent capabilities for capturing the semantics from complex RS scenes and generating the ...

    • openAccess   Deep Learning-Based Building Footprint Extraction With Missing Annotations 

      kang, jian; Fernandez-Beltran, Ruben; Sun, Xian; Ni, Jingen; Plaza, Antonio Institute of Electrical and Electronics Engineers (2021-04-21)
      Most state-of-the-art deep learning-based methods for extraction of building footprints are aimed at designing proper convolutional neural network (CNN) architectures or loss functions able to effectively predict building ...

    • openAccess   Deep Metric Learning Based on Scalable Neighborhood Components for Remote Sensing Scene Characterization 

      kang, jian; Fernandez-Beltran, Ruben; Zhen, Ye; Tong, Xiaohua; Ghamisi, Pedram; Plaza, Antonio Institute of Electrical and Electronics Engineers (2020-05-12)
      With the development of convolutional neural networks (CNNs), the semantic understanding of remote sensing (RS) scenes has been significantly improved based on their prominent feature encoding capabilities. While many ...

    • openAccess   Deep transfer learning for the recognition of types of face masks as a core measure to prevent the transmission of COVID-19 

      Mar Cupido, Ricardo; García, Vicente; Rivera-Zárate, Gilberto; Sánchez Garreta, Josep Salvador Elsevier (2022-08)
      The use of face masks in public places has emerged as one of the most effective non-pharmaceutical measures to lower the spread of COVID-19 infection. This has led to the development of several detection systems for ...

    • openAccess   Graph Relation Network: Modeling Relations Between Scenes for Multilabel Remote-Sensing Image Classification and Retrieval 

      kang, jian; Fernandez-Beltran, Ruben; Danfeng, Hong; Chanussot, Jocelyn; Plaza, Antonio IEEE (2020-08-21)
      Due to the proliferation of large-scale remote-sensing (RS) archives with multiple annotations, multilabel RS scene classification and retrieval are becoming increasingly popular. Although some recent deep learning-based ...

    • closedAccess   Improving the reliability of deep learning computational ghost imaging with prediction uncertainty based on neighborhood feature maps 

      Kataoka, Shoma; Mizutani, Yasuhiro; Ipus, Erick; Nitta, Kouichi; Matoba, Osamu; Takaya, Yasuhiro; Tajahuerce, Enrique Optica Publishing Group (2024-05-10)
      Defect inspection is required in various fields, and many researchers have attempted deep-learning algorithms for inspections. Deep-learning algorithms have advantages in terms of accuracy and measurement time; however, ...

    • closedAccess   Lightweight Hybrid CNN-ELM Model for Multi-building and Multi-floor Classification 

      Quezada Gaibor, Darwin; Torres-Sospedra, Joaquín; Nurmi, Jari; Koucheryavy, Yevgeni; Huerta, Joaquin IEEE (2022)
      Machine learning models have become an essential tool in current indoor positioning solutions, given their high capabilities to extract meaningful information from the environment. Convolutional neural networks (CNNs) ...

    • openAccess   Rotation-Invariant Deep Embedding for Remote Sensing Images 

      kang, jian; Fernandez-Beltran, Ruben; Wang, Zhirui; Sun, Xian; Ni, Jingen; Plaza, Antonio Institute of Electrical and Electronics Engineers (2021-06-28)
      Endowing convolutional neural networks (CNNs) with the rotation-invariant capability is important for characterizing the semantic contents of remote sensing (RS) images since they do not have typical orientations. Most of ...

    • closedAccess   Semi-supervised Classification for Remote Sensing Datasets 

      Hernandez-Sequeira, Itza; Fernandez-Beltran, Ruben; Xu, Yonghao; Ghamisi, Pedram; Pla, Filiberto Springer (2023-09-11)
      Deep semi-supervised learning (DSSL) is a rapidly-growing field that takes advantage of a limited number of labeled examples to leverage massive amounts of unlabeled data. The underlying idea is that training on small yet ...

    • openAccess   Spatio-temporal prediction of Baltimore crime events using CLSTM neural networks 

      Esquivel, Nicolás; Nicolis, Orietta; Peralta, Billy; Mateu, Jorge IEEE (2020-11-09)
      Crime activity in many cities worldwide causes significant damages to the lives of victims and their surrounding communities. It is a public disorder problem, and big cities experience large amounts of crime events. ...

    • openAccess   Transfer Deep Learning for Remote Sensing Datasets: A Comparison Study 

      Hernandez-Sequeira, Itza; Fernandez-Beltran, Ruben; Pla, Filiberto IEEE (2022-07-17)
      Remote sensing is also benefiting from the quick development of deep learning algorithms for image analysis and classification tasks. In this paper, we evaluate the classification performance of a well-known Convolutional ...

    • openAccess   Transfer Learning for Convolutional Indoor Positioning Systems 

      Klus, Roman; Klus, Lucie; Talvitie, Jukka; Pihlajasalo, Jaakko; Torres-Sospedra, Joaquín; Valkama, Mikko IEEE (2021-11-29)
      Fingerprinting is a widely used technique in indoor positioning, mainly due to its simplicity. Usually, this technique is used with the deterministic k - Nearest Neighbors (k-NN) algorithm. Utilizing a neural network model ...