Deep hybrid dilated residual networks for hyperspectral image classification
Sign Up to like & getrecommendations! Published in 2020 at "Neurocomputing"
DOI: 10.1016/j.neucom.2019.11.092
Abstract: Abstract This study presents a new architecture for deep convolution networks, end-to-end hybrid dilated residual networks wherein 3D cube images are input for hyperspectral image (HSI) classification, and this is termed as 3D-2D SSHDR. The… read more here.
Keywords: classification; residual networks; hybrid dilated; dilated residual ... See more keywords
Dilated residual networks with multi-level attention for speaker verification
Sign Up to like & getrecommendations! Published in 2020 at "Neurocomputing"
DOI: 10.1016/j.neucom.2020.06.079
Abstract: Abstract With the development of deep learning techniques, speaker verification (SV) systems based on deep neural network (DNN) achieve competitive performance compared with traditional i-vector-based works. Previous DNN-based SV methods usually employ time-delay neural network,… read more here.
Keywords: residual networks; speaker verification; level attention; dilated residual ... See more keywords
HiCARN: resolution enhancement of Hi-C data using cascading residual networks
Sign Up to like & getrecommendations! Published in 2022 at "Bioinformatics"
DOI: 10.1093/bioinformatics/btac156
Abstract: Abstract Motivation High throughput chromosome conformation capture (Hi-C) contact matrices are used to predict 3D chromatin structures in eukaryotic cells. High-resolution Hi-C data are less available than low-resolution Hi-C data due to sequencing costs but… read more here.
Keywords: resolution data; high resolution; cascading residual; resolution enhancement ... See more keywords
Using deep Residual Networks to search for galaxy-Ly α emitter lens candidates based on spectroscopic selection
Sign Up to like & getrecommendations! Published in 2018 at "Monthly Notices of the Royal Astronomical Society"
DOI: 10.1093/mnras/sty2708
Abstract: More than one hundred galaxy-scale strong gravitational lens systems have been found by searching for the emission lines coming from galaxies with redshifts higher than the lens galaxies. Based on this spectroscopic-selection method, we introduce… read more here.
Keywords: based spectroscopic; residual networks; networks search; spectroscopic selection ... See more keywords
Learning Optical Flow Using Deep Dilated Residual Networks
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2898988
Abstract: Nowadays, convolutional neural networks achieve remarkable performance on optical flow estimation because of its strong non-linear fitting ability. Most of them adopt the U-Net architecture, which contains an encoder part and a decoder part. In… read more here.
Keywords: learning optical; dilated residual; flow; optical flow ... See more keywords
Landslide Detection Using Residual Networks and the Fusion of Spectral and Topographic Information
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2935761
Abstract: Landslide inventories are in high demand for risk assessment of this natural hazard, particularly in tropical mountainous regions. This research designed residual networks for landslide detection using spectral (RGB bands) and topographic information (altitude, slope,… read more here.
Keywords: landslide detection; fusion; topographic information; residual networks ... See more keywords
Towards Explainable Ear Recognition Systems Using Deep Residual Networks
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Access"
DOI: 10.1109/access.2021.3109441
Abstract: This paper presents ear recognition models constructed with Deep Residual Networks (ResNet) of various depths. Due to relatively limited amounts of ear images we propose three different transfer learning strategies to address the ear recognition… read more here.
Keywords: performance; residual networks; towards explainable; deep residual ... See more keywords
Recurrent Residual Networks Contain Stronger Lottery Tickets
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Access"
DOI: 10.1109/access.2023.3245808
Abstract: Accurate neural networks can be found just by pruning a randomly initialized overparameterized model, leaving out the need for any weight optimization. The resulting subnetworks are small, sparse, and ternary, making excellent candidates for efficient… read more here.
Keywords: contain stronger; networks contain; stronger lottery; recurrent residual ... See more keywords
Spiking Deep Residual Networks.
Sign Up to like & getrecommendations! Published in 2021 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2021.3119238
Abstract: Spiking neural networks (SNNs) have received significant attention for their biological plausibility. SNNs theoretically have at least the same computational power as traditional artificial neural networks (ANNs). They possess the potential of achieving energy-efficient machine… read more here.
Keywords: resnet; performance; residual networks; neural networks ... See more keywords
Norm-Preservation: Why Residual Networks Can Become Extremely Deep?
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Pattern Analysis and Machine Intelligence"
DOI: 10.1109/tpami.2020.2990339
Abstract: Augmenting neural networks with skip connections, as introduced in the so-called ResNet architecture, surprised the community by enabling the training of networks of more than 1,000 layers with significant performance gains. This paper deciphers ResNet… read more here.
Keywords: preservation; norm preservation; residual networks; networks become ... See more keywords
WBC Image Segmentation Based on Residual Networks and Attentional Mechanisms
Sign Up to like & getrecommendations! Published in 2022 at "Computational Intelligence and Neuroscience"
DOI: 10.1155/2022/1610658
Abstract: White blood cell (WBC) morphology examination plays a crucial role in diagnosing many diseases. One of the most important steps in WBC morphology analysis is WBC image segmentation, which remains a challenging task. To address… read more here.
Keywords: segmentation based; image segmentation; wbc image; segmentation ... See more keywords