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StackNet: Stacking Parameters for Continual learning [article]

Jangho Kim, Jeesoo Kim, Nojun Kwak
2020 arXiv   pre-print
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)  
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

Siyu Tang, Mykhaylo Andriluka, Bjoern Andres, Bernt Schiele
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
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]

Matteo Poggi, Fabio Tosi, Konstantinos Batsos, Philippos Mordohai, Stefano Mattoccia
2021 arXiv   pre-print
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]

Harold Erbin, Riccardo Finotello
2020 arXiv   pre-print
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

S Vineth Ligi, Soumya Snigdha Kundu, R Kumar, R Narayanamoorthi, Khin Wee Lai, Samiappan Dhanalakshmi
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