LAUSR

Deep Supervised Hashing for Fast Image Retrieval

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2 Published in 2019 at "International Journal of Computer Vision"

DOI: 10.1007/s11263-019-01174-4

Abstract: In this paper, we present a new hashing method to learn compact binary codes for highly efficient image retrieval on large-scale datasets. While the complex image appearance variations still pose a great challenge to reliable… read more here.

Keywords: supervised hashing; image; hashing fast; image retrieval ... See more keywords

Deep supervised learning with mixture of neural networks

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1 Published in 2020 at "Artificial intelligence in medicine"

DOI: 10.1016/j.artmed.2019.101764

Abstract: Deep Neural Network (DNN), as a deep architectures, has shown excellent performance in classification tasks. However, when the data has different distributions or contains some latent non-observed factors, it is difficult for DNN to train… read more here.

Keywords: classification; network; deep supervised; mixture ... See more keywords

Novel deep supervised ML models with feature selection approach for large-scale utilities and buildings short and medium-term load requirement forecasts

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0 Published in 2020 at "Energy"

DOI: 10.1016/j.energy.2020.118477

Abstract: Abstract Accurate energy analyses and forecasts not only impact a nation’s energy stability/security and environment but also provide policymakers with a reliable framework for decision-making. The load forecast of buildings and electricity companies for the… read more here.

Keywords: feature selection; medium term; energy; deep supervised ... See more keywords

Multiexpert Adversarial Regularization for Robust and Data-Efficient Deep Supervised Learning

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1 Published in 2022 at "IEEE Access"

DOI: 10.1109/access.2022.3196780

Abstract: Deep neural networks (DNNs) can achieve high accuracy when there is abundant training data that has the same distribution as the test data. In practical applications, data deficiency is often a concern. For classification tasks,… read more here.

Keywords: supervised learning; robust data; data efficient; deep supervised ... See more keywords

Automated Diagnosis of COVID-19 using Deep Supervised Autoencoder with Multi-view Features from CT Images.

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0 Published in 2021 at "IEEE/ACM transactions on computational biology and bioinformatics"

DOI: 10.1109/tcbb.2021.3102584

Abstract: Accurate and rapid diagnosis of coronavirus disease 2019 (COVID-19) from chest CT scans is of great importance and urgency. However, radiologists have to distinguish COVID-19 pneumonia from other pneumonia in a large number of CT… read more here.

Keywords: diagnosis; supervised autoencoder; covid using; deep supervised ... See more keywords

Deep Supervised Descent Method With Multiple Seeds Generation for 3-D Tracking in Point Cloud

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2 Published in 2022 at "IEEE Transactions on Industrial Informatics"

DOI: 10.1109/tii.2021.3126391

Abstract: Three-dimensional (3-D) tracking in point cloud is a core competence of autonomous robots to perceive and forecast the environment. How to initialize bounding box seeds and optimize their position and orientation are very crucial for… read more here.

Keywords: point cloud; method; tracking point; deep supervised ... See more keywords

Automatic Segmentation of Novel Coronavirus Pneumonia Lesions in CT Images Utilizing Deep-Supervised Ensemble Learning Network

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1 Published in 2022 at "Frontiers in Medicine"

DOI: 10.3389/fmed.2021.755309

Abstract: Background: The novel coronavirus disease 2019 (COVID-19) has been spread widely in the world, causing a huge threat to the living environment of people. Objective: Under CT imaging, the structure features of COVID-19 lesions are… read more here.

Keywords: ensemble learning; learning; segmentation; supervised ensemble ... See more keywords

Deep Supervised Learning Using Local Errors

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1 Published in 2018 at "Frontiers in Neuroscience"

DOI: 10.3389/fnins.2018.00608

Abstract: Error backpropagation is a highly effective mechanism for learning high-quality hierarchical features in deep networks. Updating the features or weights in one layer, however, requires waiting for the propagation of error signals from higher layers.… read more here.

Keywords: local errors; mechanism; layer; deep supervised ... See more keywords