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StackNet: Stacking Parameters for Continual learning
[article]
2020
arXiv
pre-print
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In this paper, we propose a continual learning method that is able to learn additional tasks while retaining the performance of previously learned tasks by stacking parameters. ...
The StackNet guarantees no degradation in the performance of the previously learned tasks and the index module shows high confidence in finding the origin of an input sample. ...
Method We propose a method that efficiently stacks parameters for continual learning that satisfies three properties as stated below. Property 1. ...
arXiv:1809.02441v3
fatcat:gt5s5op5yvgkliwot5yyzrquty
A New Classification Model Based on Stacknet and Deep Learning for Fast Detection of COVID 19 Through X Rays Images
2020
2020 Fourth International Conference On Intelligent Computing in Data Sciences (ICDS)
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In this proposed work, we built a new classification model named CovStacknet and it based on StackNet metamodeling methodology combined with deep convolutional neural network as the basis for features ...
The use of machine learning models constitutes a new approach, used more and more in this field. ...
The generated vector contains 25088 real values for each input image, and will be considered as features for our StackNet classifier. ...
doi:10.1109/icds50568.2020.9268777
fatcat:mjj7wmdskjbnnjqe5pyutfaj4q
Multiple People Tracking by Lifted Multicut and Person Re-identification
2017
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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To effectively match hypotheses over longer temporal gaps we develop new deep architectures for re-identification of people. ...
We demonstrate the effectiveness of our formulation by reporting a new state-of-the-art for the MOT16 benchmark. The code and pre-trained models are publicly available 1 . ...
We stack the pair of images as well as the 14 score maps together to form a 112×224×20 input volume. ...
doi:10.1109/cvpr.2017.394
dblp:conf/cvpr/TangAAS17
fatcat:ss4u6zszinenbayhfd3n37knnm
On the Synergies between Machine Learning and Binocular Stereo for Depth Estimation from Images: a Survey
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2004.08566v1 fatcat:hnoaq6ov5zgd7kye6t5g2534iy
Throughout the years the paradigm has shifted from local, pixel-level decision to various forms of discrete and continuous optimization to data-driven, learning-based methods. ...
Recently, the rise of machine learning and the rapid proliferation of deep learning enhanced stereo matching with new exciting trends and applications unthinkable until a few years ago. ...
To this aim, the authors designed a novel monocular network architecture, called StackNet, built by stacking two-subnetworks, respectively SimpleNet, that learns to infer an initial depth estimate in a ...
arXiv:2004.08566v2
fatcat:wcwfgzibo5evbkun3atpsz6kwm
Machine learning for complete intersection Calabi-Yau manifolds: a methodological study
[article]
2020
arXiv
pre-print
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First, we obtain 97 inspired by the Inception model for the old dataset, using only 30 of the data for training. ...
feature engineering and sequential convolutional network reach at best 36 that neural networks can be valuable to study the properties of geometries appearing in string theory. ...
Possible solutions would be to use a deeper Inception network, find a better architecture including engineered features, and refine the ensembling (for example using StackNet [106] ). ...
arXiv:2007.15706v1
fatcat:ibd4hx2dhbe3lffm5e264hbsmi
Radiological Analysis of COVID-19 Using Computational Intelligence: A Broad Gauge Study
2022
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This study aimed at identifying the various COVID-19 medical imaging analysis models proposed by different researchers and featured their merits and demerits. ...
This work also tries to bring out the different unexplored research areas that are available for medical image analysis and how the vast research done for COVID-19 can advance the field. ...
[67] proposed the StackNet-DenVIS to reduce the false-negative rate in classification using a stacked generalization ensemble of four different CNNs. ...
doi:10.1155/2022/5998042
pmid:35251572
pmcid:PMC8890832
fatcat:rigcoysccrdohajd2su3vbl334