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An Efficient and Scalable Deep Learning Approach for Road Damage Detection
SadraNaddaf-sh, M-MahdiNaddaf-Sh, AmirR.Kashani....
Published date-11/18/2020
DataAugmentation, ImageAugmentation, ObjectDetection, RoadDamageDetection
Pavement condition evaluation is essential to time the preventative or rehabilitative actions and control distress propagation. Failing to conduct timely evaluations can lead to severe structural and financial loss of …
TJU-DHD: A Diverse High-Resolution Dataset for Object Detection
YanweiPang, JialeCao, YazhaoLi....
Published date-11/18/2020
ObjectDetection, PedestrianDetection
Vehicles, pedestrians, and riders are the most important and interesting objects for the perception modules of self-driving vehicles and video surveillance. However, the state-of-the-art performance of detecting such important objects …
FixBi: Bridging Domain Spaces for Unsupervised Domain Adaptation
JaeminNa, HeechulJung, HyungJinChang....
Published date-11/18/2020
DomainAdaptation, UnsupervisedDomainAdaptation
Unsupervised domain adaptation (UDA) methods for learning domain invariant representations have achieved remarkable progress. However, few studies have been conducted on the case of large domain discrepancies between a source …
Statistical model-based evaluation of neural networks
SandipanDas, PrakashB.Gohain, AlirezaM.Javid....
Published date-11/18/2020
Using a statistical model-based data generation, we develop an experimental setup for the evaluation of neural networks (NNs). The setup helps to benchmark a set of NNs vis-a-vis minimum-mean-square-error (MMSE) …
A User's Guide to Calibrating Robotics Simulators
BhairavMehta, AnkurHanda, DieterFox....
Published date-11/17/2020
DecisionMaking
Simulators are a critical component of modern robotics research. Strategies for both perception and decision making can be studied in simulation first before deployed to real world systems, saving on …
Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian Processes
QuangMinhHoang, TrongNghiaHoang, HaiPham....
Published date-11/17/2020
GaussianProcesses
We introduce a new scalable approximation for Gaussian processes with provable guarantees which hold simultaneously over its entire parameter space. Our approximation is obtained from an improved sample complexity analysis …