Results 31 to 40 of about 788,437 (264)
Multi-Scale Residual Convolutional Neural Network for Haze Removal of Remote Sensing Images
Remote Sensing, 2018 Haze removal is a pre-processing step that operates on at-sensor radiance data prior to the physically based image correction step to enhance hazy imagery visually.
Hou Jiang, Ning Lu
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Spectrum Sensing Method Based on Residual Cellular Network
IEEE Access, 2022 The traditional spectrum sensing method based on convolutional neural network (CNN) has the single-branch convolutional network structure and the shallow network structure which limits the ability of extracting the Primary User (PU) feature.
Jianxin Gai +3 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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A Comparative Analysis of Residual Block Alternatives for End-to-End Audio Classification
IEEE Access, 2020 Residual learning is known for being a learning framework that facilitates the training of very deep neural networks. Residual blocks or units are made up of a set of stacked layers, where the inputs are added back to their outputs with the aim of ...
Javier Naranjo-Alcazar +5 more
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IEEE Access, 2020 Semantic segmentation is one of the most commonly used techniques for road scene understanding. Recently developed deep learning-based semantic segmentation networks are typically based on the encoder-decoder structure and have made great progress in ...
Jee-Young Sun +2 more
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Residual Learning for Marine Mammal Classification
IEEE Access, 2022 The passive acoustic monitoring of marine mammals is an essential tool for researchers tracking the populations of individual species in threatened environments. Given the large quantity of audio data generated by passive acoustic arrays, it is desirable
Daniel T. Murphy +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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Residual Forward-Subtracted U-Shaped Network for Dynamic and Static Image Restoration
IEEE Access, 2020 Advanced image sensors with high resolution are now being developed for specially purposed electro-optical systems, with research focused on robust image quality performance in terms of super resolution and noise removal under various environmental ...
Ho Min Jung +2 more
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Applied Sciences, 2021 Segmentation of street scenes is a key technology in the field of autonomous vehicles. However, conventional segmentation methods achieve low accuracy because of the complexity of street landscapes.
Seokyong Shin, Sanghun Lee, Hyunho Han
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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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