Opportunities and challenges in deep learning adversarial robustness: A survey
SH Silva, P Najafirad - arXiv preprint arXiv:2007.00753, 2020 - arxiv.org
As we seek to deploy machine learning models beyond virtual and controlled domains, it is
critical to analyze not only the accuracy or the fact that it works most of the time, but if such a …
critical to analyze not only the accuracy or the fact that it works most of the time, but if such a …
Learning dynamic routing for semantic segmentation
Recently, numerous handcrafted and searched networks have been applied for semantic
segmentation. However, previous works intend to handle inputs with various scales in pre …
segmentation. However, previous works intend to handle inputs with various scales in pre …
Kiu-net: Towards accurate segmentation of biomedical images using over-complete representations
JMJ Valanarasu, VA Sindagi, I Hacihaliloglu… - … Image Computing and …, 2020 - Springer
Due to its excellent performance, U-Net is the most widely used backbone architecture for
biomedical image segmentation in the recent years. However, in our studies, we observe …
biomedical image segmentation in the recent years. However, in our studies, we observe …
VNet: An end-to-end fully convolutional neural network for road extraction from high-resolution remote sensing data
A Abdollahi, B Pradhan, A Alamri - Ieee Access, 2020 - ieeexplore.ieee.org
One of the most important tasks in the advanced transportation systems is road extraction.
Extracting road region from high-resolution remote sensing imagery is challenging due to …
Extracting road region from high-resolution remote sensing imagery is challenging due to …
Scene-driven multitask parallel attention network for building extraction in high-resolution remote sensing images
The application of convolutional neural networks has been shown to significantly improve
the accuracy of building extraction from very high-resolution (VHR) remote sensing images …
the accuracy of building extraction from very high-resolution (VHR) remote sensing images …
Linguistic structure guided context modeling for referring image segmentation
Referring image segmentation aims to predict the foreground mask of the object referred by
a natural language sentence. Multimodal context of the sentence is crucial to distinguish the …
a natural language sentence. Multimodal context of the sentence is crucial to distinguish the …
CloudScout: A deep neural network for on-board cloud detection on hyperspectral images
The increasing demand for high-resolution hyperspectral images from nano and
microsatellites conflicts with the strict bandwidth constraints for downlink transmission. A …
microsatellites conflicts with the strict bandwidth constraints for downlink transmission. A …
Building realistic structure models to train convolutional neural networks for seismic structural interpretation
Seismic structural interpretation involves highlighting and extracting faults and horizons that
are apparent as geometric features in a seismic image. Although seismic image processing …
are apparent as geometric features in a seismic image. Although seismic image processing …
Understanding important features of deep learning models for segmentation of high-resolution transmission electron microscopy images
Cutting edge deep learning techniques allow for image segmentation with great speed and
accuracy. However, application to problems in materials science is often difficult since these …
accuracy. However, application to problems in materials science is often difficult since these …
Wavelet integrated CNNs for noise-robust image classification
Abstract Convolutional Neural Networks (CNNs) are generally prone to noise interruptions,
ie, small image noise can cause drastic changes in the output. To suppress the noise effect …
ie, small image noise can cause drastic changes in the output. To suppress the noise effect …