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Adaptive Region-Based Active Learning [article]

Corinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, Ningshan Zhang
2020 arXiv   pre-print
We present a new active learning algorithm that adaptively partitions the input space into a finite number of regions, and subsequently seeks a distinct predictor for each region, both phases actively  ...  We also report the results of an extensive suite of experiments on several real-world datasets demonstrating substantial empirical benefits over existing single-region and non-adaptive region-based active  ...  , and then runs region-based active learning on these regions.  ... 
arXiv:2002.07348v1 fatcat:lmtzxz3efzcwlmzroztwi3ceta

Region-Based Active Learning with Hierarchical and Adaptive Region Construction [chapter]

Zhipeng Luo, Milos Hauskrecht
2019 Proceedings of the 2019 SIAM International Conference on Data Mining  
To quickly discover pure regions (in terms of class proportion) in the data, we have developed a novel active learning framework that constructs regions in a hierarchical and adaptive way.  ...  To solve this problem, instead of soliciting instance-based annotation we explore region-based annotation as the human feedback.  ...  The most recent work is [LH18b] which develops a region-based active learning framework called HALR (Hierarchical Active Learning with proportion feedback on Regions) that actively constructs a hierarchy  ... 
doi:10.1137/1.9781611975673.50 pmid:31929950 pmcid:PMC6953978 dblp:conf/sdm/LuoH19 fatcat:ubsn5pdskraineqrpojqv5bmoq

Regional Active Contours based on Variational level sets and Machine Learning for Image Segmentation [article]

M. Abdelsamea
2015 arXiv   pre-print
Active Contour Models (ACMs) constitute a powerful energy-based minimization framework for image segmentation, which relies on the concept of contour evolution.  ...  Experimental results demonstrate the high accuracy of the segmentation results, obtained by the proposed models on various benchmark synthetic and real images compared with state-of-the-art active contour  ...  SOM-based Chan-Vese (SOMCV) model. SOMCV model SOM-based Regional Active Contour (SOM-RAC) model.  ... 
arXiv:1511.00111v1 fatcat:lba7gzjkivdn5a5hueow33t3bi

CEREALS - Cost-Effective REgion-based Active Learning for Semantic Segmentation [article]

Radek Mackowiak, Philip Lenz, Omair Ghori, Ferran Diego, Oliver Lange, Carsten Rother
2018 arXiv   pre-print
We propose an active learning-based strategy, called CEREALS, in which a human only has to hand-label a few, automatically selected, regions within an unlabeled image corpus.  ...  The automatic selection procedure is achieved by: a) using a suitable information measure combined with an estimate about human annotation effort, which is inferred from a learned cost model, and b) exploiting  ...  A Supplementary Material CEREALS -Cost-Effective REgion-based Active Learning for Semantic Segmentation A.1 Implementation Details Instead of cropping the annotated regions out of the images, while taking  ... 
arXiv:1810.09726v1 fatcat:v46pm42rqba2pga2czx2dwgv3m

ReDAL: Region-based and Diversity-aware Active Learning for Point Cloud Semantic Segmentation [article]

Tsung-Han Wu, Yueh-Cheng Liu, Yu-Kai Huang, Hsin-Ying Lee, Hung-Ting Su, Ping-Chia Huang, Winston H. Hsu
2021 arXiv   pre-print
To reduce the huge annotation burden, we propose a Region-based and Diversity-aware Active Learning (ReDAL), a general framework for many deep learning approaches, aiming to automatically select only informative  ...  Extensive experiments show that our method highly outperforms previous active learning strategies, and we achieve the performance of 90% fully supervised learning, while less than 15% and 5% annotations  ...  To the best of our knowledge, we are the first to design a region-based active learning framework general for many deep learning models.  ... 
arXiv:2107.11769v2 fatcat:ttng56xpcrbilcmkgpqmsg4wbi

MetaBox+: A new Region Based Active Learning Method for Semantic Segmentation using Priority Maps [article]

Pascal Colling, Lutz Roese-Koerner, Hanno Gottschalk, Matthias Rottmann
2020 arXiv   pre-print
We present a novel region based active learning method for semantic image segmentation, called MetaBox+.  ...  We compare our method to entropy based methods, where we consider the entropy as uncertainty of the prediction.  ...  Region based Active Learning In this section we first describe a region based AL method, which queries fixed-size and quadratic image regions. Afterwards, we describe our new AL method.  ... 
arXiv:2010.01884v1 fatcat:6avfw2pvvbgx7kwxfvpg3tg5ci

A Hearing-Model-Based Active-Learning Test for the Determination of Dead Regions

Josef Schlittenlacher, Richard E. Turner, Brian C. J. Moore
2018 Trends in Hearing  
This article describes a Bayesian active-learning procedure for estimating the edge frequency, f e , of a dead region, that is, a region in the cochlea with no or very few functioning inner hair cells  ...  The method is based on the psychophysical tuning curve (PTC) but estimates the shape of the PTC from the parameters of a hearing model, namely f e , and degree of outer hair cell loss.  ...  The value of HL total ( f e ) was based on the audiogram obtained using the active-learning procedure. The value of OHCL( f e ) was a second free parameter.  ... 
doi:10.1177/2331216518788215 pmid:30022735 pmcid:PMC6053858 fatcat:mm6t3xjxajhv5fxfhmgyv5pbf4

A Weakly Supervised Region-Based Active Learning Method for COVID-19 Segmentation in CT Images [article]

Issam Laradji, Pau Rodriguez, Frederic Branchaud-Charron, Keegan Lensink, Parmida Atighehchian, William Parker, David Vazquez, Derek Nowrouzezahrai
2020 arXiv   pre-print
We address this challenge introducing a scalable, fast, and accurate active learning system that accelerates the labeling of CT scan images.  ...  Our experiments on open-source COVID-19 datasets show that using an entropy-based method to rank unlabeled regions yields to significantly better results than random labeling of these regions.  ...  Figure 2 : 2 Prediction comparison between fully supervised and region-based active learning system.  ... 
arXiv:2007.07012v1 fatcat:3ckyri5odrbxxlohq2wjartukq

A Brief Discussion on the Design of Activity View of High School English Learning under Task-based Teaching Method

Aiping Zhang
2020 Region - Educational Research and Reviews  
Therefore, it is particularly important to integrate the Task-based language teaching (TBLT) and Activity View of English Learning to design high school English teaching.  ...  Under the new curriculum reform, it advocates Activity View of English Learning for the development of discipline core literacy.  ...  DOI: 10.32629/RERR.V2I3.145 Region -Educational Research and Reviews 48 4.2 Teaching design of activity view of English learning Design of activity view of English learning under TBLT The design of activity  ... 
doi:10.32629/rerr.v2i3.145 fatcat:lkzaidmbsrabvk5o6b3gylad6q

Preparing Smart Students Through Performing Problem Based Learning Activities Within University-Industry Collaborative Knowledge Sharing Platform

Lal Mohan Baral, Lucian Lobonţ, Bogdan Chiliban, Vlad Dorin
2015 Balkan Region Conference on Engineering and Business Education  
The purpose of this paper is to explore a modified problem based and project based learning (PBL) approach, which helps to develop smart students through acquiring knowledge using various sources from  ...  During last few decades, different teaching and learning approaches have been proposed around the world aiming to enhance the student's knowledge acquiring ability.  ...  Moreover, the PBL-concept representing a learning philosophy of experiential, experimental, contextual, situated, social and team-based activities, which can be modeled in many ways in preparing the curriculum  ... 
doi:10.1515/cplbu-2015-0010 fatcat:tptr72rpvze4hcy7ksj42chg4y

Machine Learning Classification of Cirrhotic Patients with and without Minimal Hepatic Encephalopathy Based on Regional Homogeneity of Intrinsic Brain Activity

Qiu-Feng Chen, Hua-Jun Chen, Jun Liu, Tao Sun, Qun-Tai Shen, Han-Chieh Lin
2016 PLoS ONE  
This study represents the first attempt to discriminate MHE in cirrhotic patients based on examination of regional homogeneity of brain intrinsic activity and machine learning.  ...  Machine learning-based approaches have been used to examine fMRI data in a multivariate manner and extract features predictive of disease-related membership.  ...  Third, we only examined the potential of altered regional brain intrinsic activity in discriminating between NHE and MHE groups.  ... 
doi:10.1371/journal.pone.0151263 pmid:26978777 pmcid:PMC4792397 fatcat:jtdzzc4dwrhcjd3wkftorafsoa

Activity-Based Teaching and Learning Approach and Students' Academic Performance: Evidence from Among Stakeholders from Barekese District Ashanti Region of Ghana

Theophilus Apenuvor, Frank Yao Gbadago, Kwadwo Ankomah, Agnes Fafa Anthony
2021 International journal of educational studies  
In this study, the authors explored the level of awareness and views on the activity-based approach to teaching and learning (ABTLA) in enhancing skills, competency and academic performance among students  ...  and other stakeholders from Senior High Schools (SHS) and Colleges of Education (COE) in Barekese District of Ashanti Region of Ghana.  ...  Activity-based learning has been promoted as an alternative to more traditional, teacher-centered instruction.  ... 
doi:10.53935/2641-533x.v4i2.156 fatcat:lkiabg5y5fgldovfgpiyam76aa

Segmentation of the endocardial wall of the left atrium using local region-based active contours and statistical shape learning

Yi Gao, Behnood Gholami, Robert S. MacLeod, Joshua Blauer, Wassim M. Haddad, Allen R. Tannenbaum, Benoit M. Dawant, David R. Haynor
2010 Medical Imaging 2010: Image Processing  
As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this paper we use shape learning and shape-based image segmentation to identify the endocardial  ...  Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region.  ...  Here, we use the mean shapeΦ as the initialization for the localized region-based active contour.  ... 
doi:10.1117/12.844321 dblp:conf/miip/GaoGMBHT10 fatcat:psfiufanfjfg3hkvhojdtgiigy

Regional active contours based on variational level sets and machine learning for image segmentation

Mohammed Abdelsamea
2015
Active Contour Models (ACMs) constitute a powerful energy-based minimization framework for image segmentation, which relies on the concept of contour evolution.  ...  Experimental results demonstrate the high accuracy of the segmentation results, obtained by the proposed models on various benchmark synthetic and real images compared with state-of-the-art active contour  ...  SOM-based Regional Active Contour (SOM-RAC) model.  ... 
doi:10.6092/imtlucca/e-theses/160 fatcat:op7daw33vfajxp3yhuio47j46a

Theory of Disagreement-Based Active Learning

Steve Hanneke
2014 Foundations and Trends® in Machine Learning  
Again, the disagreement-based active learning strategy would request the label of a new point if and only if it is inside the shaded region.  ...  Throughout much of the article, we will focus on one particular active learning technique, known as disagreement-based active learning.  ... 
doi:10.1561/2200000037 fatcat:6fpwv75i2rbi3lx6z52uq3zybq
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