Competition on robust deep learning
Sign Up to like & getrecommendations! Published in 2023 at "National Science Review"
DOI: 10.1093/nsr/nwad087
Abstract: This perspective paper proposes a new adversarial training method based on large-scale pre-trained models to achieve state-of-the-art adversarial robustness on ImageNet. read more here.
Keywords: robust deep; deep learning; competition robust;
DeepClean: A Robust Deep Learning Technique for Autonomous Vehicle Camera Data Privacy
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3222834
Abstract: Autonomous Vehicles (AVs) are equipped with several sensors which produce various forms of data, such as geo-location, distance, and camera data. The volume and utility of these data, especially camera data, have contributed to the… read more here.
Keywords: camera; robust deep; privacy; deep learning ... See more keywords
Robust Deep Learning Seismic Velocity Inversion Over Varying Acquisitions
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2023.3250121
Abstract: Retrieving subsurface velocity information from recorded seismograms generally involves solving an inverse problem using various optimization methods. Deep learning (DL) has recently become an emerging alternative technique to provide solutions for such velocity inversion tasks.… read more here.
Keywords: velocity inversion; robust deep; velocity; deep learning ... See more keywords
Robust Deep Sensing Through Transfer Learning in Cognitive Radio
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Wireless Communications Letters"
DOI: 10.1109/lwc.2019.2940579
Abstract: We propose a robust spectrum sensing framework based on deep learning. The received signals at the secondary user’s receiver are filtered, sampled and then directly fed into a convolutional neural network. Although this deep sensing… read more here.
Keywords: deep sensing; learning cognitive; robust deep; sensing transfer ... See more keywords
Robust Deep Learning for IC Test Problems
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems"
DOI: 10.1109/tcad.2021.3054808
Abstract: Numerous machine learning (ML), and more recently, deep-learning (DL)-based approaches, have been proposed to tackle scalability issues in electronic design automation, including those in integrated circuit (IC) test. This article examines state-of-the-art DL for IC… read more here.
Keywords: learning test; robust deep; test; test problems ... See more keywords
CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on pattern analysis and machine intelligence"
DOI: 10.1109/tpami.2023.3271451
Abstract: Modern deep neural networks (DNNs) can easily overfit to biased training data containing corrupted labels or class imbalance. Sample re-weighting methods are popularly used to alleviate this data bias issue. Most current methods, however, require… read more here.
Keywords: sample; robust deep; class; deep learning ... See more keywords
Robust Deep Speaker Recognition: Learning Latent Representation with Joint Angular Margin Loss
Sign Up to like & getrecommendations! Published in 2020 at "Applied Sciences"
DOI: 10.3390/app10217522
Abstract: Speaker identification is gaining popularity, with notable applications in security, automation, and authentication. For speaker identification, deep-convolutional-network-based approaches, such as SincNet, are used as an alternative to i-vectors. Convolution performed by parameterized sinc functions in… read more here.
Keywords: sincnet; loss; speaker; speaker recognition ... See more keywords