Results 31 to 40 of about 21,150,821 (215)
Loss-of-function analysis of EphA receptors in retinotectal mapping [PDF]
, 2004 Peer reviewedPublisher ...
DeChiara, Thomas M. +7 more
core +1 more source
Stein-Rule Estimation under an Extended Balanced Loss Function [PDF]
, 2007 This paper extends the balanced loss function to a more general set up. The ordinary least squares and Stein-rule estimators are exposed to this general loss function with quadratic loss structure in a linear regression model.
---, Shalabh +2 more
core +1 more source
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Cloud and cloud shadow segmentation are fundamental processes in optical remote sensing image analysis. Current methods for cloud/shadow identification in geospatial imagery are not as accurate as they should, especially in the presence of snow and haze.
S. Mohajerani, Parvaneh Saeedi
semanticscholar +1 more source
Low-Dose CT Image Denoising with Improving WGAN and Hybrid Loss Function
Computational and Mathematical Methods in Medicine, 2021 The X-ray radiation from computed tomography (CT) brought us the potential risk. Simply decreasing the dose makes the CT images noisy and diagnostic performance compromised. Here, we develop a novel denoising low-dose CT image method.
Zhihua Li +6 more
semanticscholar +1 more source
Safety Maintains Lean Sustainability and Increases Performance through Fault Control
Applied Sciences, 2020 Almost every industrial and service enterprise adopts some form of Environmental Health and Safety (HSE) practices. However, there is no unified measurement implementation framework to resist losses exacerbated due to the “lack of safety precautions ...
Samia Elattar +2 more
doaj +1 more source
DC Proximal Newton for Non-Convex Optimization Problems [PDF]
, 2015 We introduce a novel algorithm for solving learning problems where both the loss function and the regularizer are non-convex but belong to the class of difference of convex (DC) functions.
Flamary, Remi +2 more
core +4 more sources
Pan-Sharpening Based on Convolutional Neural Network by Using the Loss Function With No-Reference
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 In order to preserve the spatial and spectral information of the original panchromatic and multispectral images, this article designs a loss function suitable for pan-sharpening and a four-layer convolutional neural network that could adequately extract ...
Zhangxi Xiong +3 more
semanticscholar +1 more source
A General and Adaptive Robust Loss Function [PDF]
Computer Vision and Pattern Recognition, 2017 We present a generalization of the Cauchy/Lorentzian, Geman-McClure, Welsch/Leclerc, generalized Charbonnier, Charbonnier/pseudo-Huber/L1-L2, and L2 loss functions.
J. Barron
semanticscholar +1 more source
Scale-Sensitive IOU Loss: An Improved Regression Loss Function in Remote Sensing Object Detection
IEEE Access, 2021 Regression loss function in object detection model plays an important factor during training procedure. The IoU based loss functions, such as CIOU loss, achieve remarkable performance, but still have some inherent shortages that may cause slow ...
Shuangjiang Du, Baofu Zhang, Pin Zhang
semanticscholar +1 more source
Y-Net: Multi-Scale Feature Aggregation Network With Wavelet Structure Similarity Loss Function For Single Image Dehazing [PDF]
IEEE International Conference on Acoustics, Speech, and Signal Processing, 2020 Single image dehazing is the ill-posed two-dimensional signal reconstruction problem. Recently, deep convolutional neural networks (CNN) have been successfully used in many computer vision problems. In this paper, we propose a Y-net that is named for its
Hao Yang, Chao-Han Huck Yang, Y. Tsai
semanticscholar +1 more source