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Calibrating Deep Neural Networks using Focal Loss
[article]
2020
arXiv
pre-print
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Miscalibration - a mismatch between a model's confidence and its correctness - of Deep Neural Networks (DNNs) makes their predictions hard to rely on. ...
We show that, as opposed to the standard cross-entropy loss, focal loss [Lin et. al., 2017] allows us to learn models that are already very well calibrated. ...
Appendix: Calibrating Deep Neural Networks using Focal Loss In §A, we provide some empirical evidence for the observation made in §3 in the main paper using reliability plots. ...
arXiv:2002.09437v2
fatcat:rp3zep7fyren5j6jcyuanwwfe4
A Calibrated Multiexit Neural Network for Detecting Urothelial Cancer Cells
2021
Computational and Mathematical Methods in Medicine
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calibrated than a baseline deep convolutional network. ...
Deep convolutional networks have become a powerful tool for medical imaging diagnostic. ...
Acknowledgments This work was supported in part by the project Detection of urothelial cancer cells using deep convolutional neural networks (Progetti di Ricerca Sapienza). ...
doi:10.1155/2021/5569458
pmid:34234839
pmcid:PMC8216797
fatcat:algargklsrabheapnckphabwmi
Dynamically Weighted Balanced Loss: Class Imbalanced Learning and Confidence Calibration of Deep Neural Networks
2021
IEEE Transactions on Neural Networks and Learning Systems
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We further show that the proposed loss function is classification calibrated. ...
To address the class distribution imbalance in deep learning, we propose a class rebalancing strategy based on a class-balanced dynamically weighted loss function where weights are assigned based on the ...
DEEP NEURAL NETWORK PRELIMINARIES Through several underlying network blocks or layers, Deep Neural Networks (DNNs) extract representative features and hidden structural knowledge from data automatically ...
doi:10.1109/tnnls.2020.3047335
pmid:33444149
fatcat:c3y7y4p4afdppbo5pm4dgbjhpi
Maximum Entropy on Erroneous Predictions (MEEP): Improving model calibration for medical image segmentation
[article]
2021
arXiv
pre-print
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Modern deep neural networks have achieved remarkable progress in medical image segmentation tasks. ...
Our method is agnostic to the neural architecture, does not increase model complexity and can be coupled with multiple segmentation loss functions. ...
Furthermore, we also compare our method to stateof-the-art approaches that have proven to provide better calibrated deep neural networks. ...
arXiv:2112.12218v1
fatcat:kegmdn6obrb5rfx3jyrhva3vka
Camera Calibration through Camera Projection Loss
[article]
2022
arXiv
pre-print
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2022 pre-print arXiv:2110.03479v2 fatcat:oes3lnn4rva2pkpphd5qs4c7guOther Versions
We estimate the desired parameters via novel camera projection loss (CPL) that uses the camera model neural network to reconstruct the 3D points and uses the reconstruction loss to estimate the camera ...
Our code and generated dataset are available at https://github.com/thanif/Camera-Calibration-through-Camera-Projection-Loss. ...
We train a convolutional neural network to predict the extrinsic and intrinsic camera parameters. ...
arXiv:2110.03479v3
fatcat:gbwqjow2yzfcdcytwwn7bvinra
Single Image Automatic Radial Distortion Compensation Using Deep Convolutional Network
[article]
2021
arXiv
pre-print
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Keywords: Deep Convolutional Neural Network, Radial Distortion, Single Image Rectification ...
We present a novel method for single-image automatic lens distortion compensation based on deep convolutional neural networks, capable of real-time performance and accuracy using two highest-order coefficients ...
We train our deep neural network to directly estimate the values of k2 . Network architecture. ...
arXiv:2112.08198v1
fatcat:df6o4xbstncgvce2ineglxwo7i
DEEPFOCAL: A method for direct focal length estimation
2015
2015 IEEE International Conference on Image Processing (ICIP)
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In this work, we explore the application of a deep convolutional neural network, trained on natural images obtained from Internet photo collections, to directly estimate the focal length using only raw ...
or a calibration grid, to occur in the field of view. ...
We proposed a fast method which overcomes these limitations by directly estimating the focal length from raw pixels using a deep convolutional neural network. ...
doi:10.1109/icip.2015.7351024
dblp:conf/icip/WorkmanGZBJ15
fatcat:ygjq3dwtz5g3hpd4jxijdavtbu
Soft Calibration Objectives for Neural Networks
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2108.00106v1 fatcat:ubdw5skcs5cwvbqva5ykeivduu
However, deep neural networks are often under- or over-confident in their predictions. ...
Overall, experiments across losses and datasets demonstrate that using calibration-sensitive procedures yield better uncertainty estimates under dataset shift than the standard practice of using a cross ...
Acknowledgements The authors thank Brennan McConnell and Mohammad Khajah who conducted initial explorations of soft binning calibration loss. ...
arXiv:2108.00106v2
fatcat:oxgu3qitdzb55b7lehp5ces5ii
A comprehensive study on the prediction reliability of graph neural networks for virtual screening
[article]
2020
arXiv
pre-print
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Prediction models based on deep neural networks are increasingly gaining attention for fast and accurate virtual screening systems. ...
We investigate the effects of model architectures, regularization methods, and loss functions on the prediction performance and reliability of classification results. ...
for deep neural networks. ...
arXiv:2003.07611v1
fatcat:ntyjvwu4dzfvzemixhuyaotmpm
Identifying and Exploiting Structures for Reliable Deep Learning
[article]
2021
arXiv
pre-print
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To do this, we identify structures in deep neural networks that can be exploited to mitigate the above causes of unreliability of deep learning algorithms. ...
The extraordinary performance of these systems often gives the impression that they can be used to revolutionise our lives for the better. ...
Approaches for calibrating deep neural networks Multiple approaches have been proposed in the literature for calibrating a neural network in practice. ...
arXiv:2108.07083v1
fatcat:lducrn5tlfeqvpxevz6gukfvse
Assignment of Focus Position with Convolutional Neural Networks in Adaptive Lens Based Axial Scanning for Confocal Microscopy
2022
Applied Sciences
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However, maybe the training procedure of the neural network must be adapted for some use cases. ...
Here, we introduce an alternative approach which provides a single shot estimation of the current axial focus position by a convolutional neural network. ...
Deep Single Image Camera Calibration with Radial
Distortion. ...
doi:10.3390/app12020661
fatcat:yptctmp5krgb7oz6abwcy2xroa
SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud
[article]
2018
arXiv
pre-print
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Earlier work demonstrates the promise of deep-learning-based approaches for point cloud segmentation; however, these approaches need to be improved to be practically useful. ...
With improved model structure, training loss, batch normalization and additional input channel, SqueezeSegV2 achieves significant accuracy improvement when trained on real data. ...
ACKNOWLEDGEMENT This work is partially supported by Berkeley Deep Drive (BDD), and partially sponsored by individual gifts from Intel and Samsung. ...
arXiv:1809.08495v1
fatcat:jse7z2stzbgr5exvy5k3qw4vxi
High Precision Calibration Algorithm for Binocular Stereo Vision Camera using Deep Reinforcement Learning
2022
Computational Intelligence and Neuroscience
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The deep learning fitting value function is used based on the internal parameters. ...
To address the issue of insufficient precision, therefore, a high precision calibration algorithm for binocular stereo vision camera using deep reinforcement learning is proposed. ...
neural networks. ...
doi:10.1155/2022/6596868
pmid:35401726
pmcid:PMC8989564
fatcat:fboyxxthdbffrklfq4td4oiy5a
AnatomyNet: Deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy
2018
Medical Physics (Lancaster)
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2018 pre-print arXiv:1808.05238v1 fatcat:zgpwjvydfnfy7bpejfk2qjgqreOther Versions
function combining Dice scores and focal loss to facilitate the training of the neural model. ...
Results: We collected 261 HaN CT images to train AnatomyNet, and used MICCAI Head and Neck Auto Segmentation Challenge 2015 as a benchmark dataset to evaluate the performance of AnatomyNet. ...
Ibragimov and Xing proposed a simple convolutional neural network for atlas-free deep learning based OARs segmentation [26] . ...
doi:10.1002/mp.13300
fatcat:n7nqws6jdjbqrfkqdjzpu3njde
Effect Of Personalized Calibration On Gaze Estimation Using Deep-Learning
[article]
2021
arXiv
pre-print
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We trained a multi modal convolutional neural network and analysed its performance with and without calibration and this evaluation provides clear insights on how calibration improved the performance of ...
To analyse the performance in such scenarios we have tried to simulate a calibration mechanism. In this work we use the MPIIGaze data set. ...
Thus with the neural network we are solving an optimisation problem of minimising this loss function The normalized data set is available online for public use. ...
arXiv:2109.12801v1
fatcat:dirot2hdirf3thgxavglxrulgq
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