Results 31 to 40 of about 792,416 (284)
Deep Residual Autoencoder with Multiscaling for Semantic Segmentation of Land-Use Images
Remote Sensing, 2019 Semantic segmentation is a fundamental means of extracting information from remotely sensed images at the pixel level. Deep learning has enabled considerable improvements in efficiency and accuracy of semantic segmentation of general images.
Lianfa Li
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Cross-media residual correlation learning [PDF]
Applied Informatics, 2017 نظرًا لتقدم الشبكات العصبية العميقة (DNN)، تم توظيف DNN لاسترجاع الوسائط المتقاطعة. يمكن لطرق الاسترجاع عبر الوسائط الحالية القائمة على DNN تحويل التمثيل المنفصل لكل نوع وسائط إلى تمثيل مشترك من خلال القيود بين الوسائط وداخل الوسائط. باستخدام التمثيل المشترك، يمكننا قياس أوجه التشابه بين الحالات غير المتجانسة وإجراء استرجاع عبر الوسائط.
Mingkuan Yuan, Xin Huang, Yuxin Peng
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Learning an Efficient Convolution Neural Network for Pansharpening
Algorithms, 2019 Pansharpening is a domain-specific task of satellite imagery processing, which aims at fusing a multispectral image with a corresponding panchromatic one to enhance the spatial resolution of multispectral image.
Yecai Guo, Fei Ye, Hao Gong
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Enhanced Lightweight Multiscale Convolutional Neural Network for Rolling Bearing Fault Diagnosis
IEEE Access, 2020 The vibration signals collected from rolling bearings in industrial systems are highly complex and contain intense environmental noise, which challenges the performance of traditional fault diagnosis methods.
Yaowei Shi +5 more
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A Deep Residual Learning Implementation of Metamorphosis
2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), 2022 ISBI ...
Maillard, Matthis +4 more
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Sensors, 2020 Intelligent fault diagnosis algorithm for rolling bearings has received increasing attention. However, in actual industrial environments, most rolling bearings work under severe working conditions of variable speed and strong noise, which makes the ...
Hongmei Shi +3 more
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Residual matrix product state for machine learning
SciPost Physics, 2023 Tensor network, which originates from quantum physics, is emerging as an efficient tool for classical and quantum machine learning. Nevertheless, there still exists a considerable accuracy gap between tensor network and the sophisticated neural network models for classical machine learning.
Meng, Ye-Ming +4 more
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A Multi-Sensor Fusion Framework Based on Coupled Residual Convolutional Neural Networks
Remote Sensing, 2020 Multi-sensor remote sensing image classification has been considerably improved by deep learning feature extraction and classification networks. In this paper, we propose a novel multi-sensor fusion framework for the fusion of diverse remote sensing data
Hao Li +6 more
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Gait Recognition With Wearable Sensors Using Modified Residual Block-Based Lightweight CNN
IEEE Access, 2022 Gait recognition with wearable sensors is an effective approach to identifying people by recognizing their distinctive walking patterns. Deep learning-based networks have recently emerged as a promising technique in gait recognition, yielding better ...
Md. Al Mehedi Hasan +3 more
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ResLT: Residual Learning for Long-tailed Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 Deep learning algorithms face great challenges with long-tailed data distribution which, however, is quite a common case in real-world scenarios. Previous methods tackle the problem from either the aspect of input space (re-sampling classes with different frequencies) or loss space (re-weighting classes with different weights), suffering from heavy ...
Cui, Jiequan +4 more
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