Semi-supervised learning: a brief review

Reddy, Y. C. A. Padmanabha; Viswanath, Pramod; Reddy, B. Eswara

doi:10.14419/ijet.v7i1.8.9977

Cited by 90 publications

(20 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Learning addresses this problem and act as a half way between supervised and unsupervised learning [67,68]. Normally, first, these type of algorithms use the limited set of labelled samples to train themselves, resulting in "partially trained" models.…”

Section: Semi-supervised Learningmentioning

confidence: 99%

Data-Driven Fault Diagnosis for Electric Drives: A Review

Gonzalez-Jimenez

Olmo

Poza

et al. 2021

Sensors

View full text Add to dashboard Cite

The need to manufacture more competitive equipment, together with the emergence of the digital technologies from the so-called Industry 4.0, have changed many paradigms of the industrial sector. Presently, the trend has shifted to massively acquire operational data, which can be processed to extract really valuable information with the help of Machine Learning or Deep Learning techniques. As a result, classical Condition Monitoring methodologies, such as model- and signal-based ones are being overcome by data-driven approaches. Therefore, the current paper provides a review of these data-driven active supervision strategies implemented in electric drives for fault detection and diagnosis (FDD). Hence, first, an overview of the main FDD methods is presented. Then, some basic guidelines to implement the Machine Learning workflow on which most data-driven strategies are based, are explained. In addition, finally, the review of scientific articles related to the topic is provided, together with a discussion which tries to identify the main research gaps and opportunities.

show abstract

Section: Semi-supervised Learningmentioning

confidence: 99%

Data-Driven Fault Diagnosis for Electric Drives: A Review

Gonzalez-Jimenez

Olmo

Poza

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Other works forgo this aggregation, querying single tokens at a time (Tomanek and Hahn, 2009;Wanvarie et al, 2011;Marcheggiani and Artières, 2014). These works show that AL for NER can be improved by taking the single token as a unit query, and use semi-supervision (Reddy et al, 2018;Iscen et al, 2019) for training on partially labelled sentences (Muslea et al, 2002). However, querying single-tokens is inapplicable in practise because, either a) annotators have access to the full sentence when queried but can only label one token, which would lead to frustration as they are asked to read the full sentence but only annotate a single token, or b) annotators only have access to the token of interest, which means that they would not have enough information to label tokens differently based on their context, leading to annotators labeling any unique token with the same label.…”

Section: Related Workmentioning

confidence: 99%

Subsequence Based Deep Active Learning for Named Entity Recognition

Radmard¹,

Fathullah²,

Lipani³

2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

View full text Add to dashboard Cite

Active Learning (AL) has been successfully applied to Deep Learning in order to drastically reduce the amount of data required to achieve high performance. Previous works have shown that lightweight architectures for Named Entity Recognition (NER) can achieve optimal performance with only 25% of the original training data. However, these methods do not exploit the sequential nature of language and the heterogeneity of uncertainty within each instance, requiring the labelling of whole sentences. Additionally, this standard method requires that the annotator has access to the full sentence when labelling. In this work, we overcome these limitations by allowing the AL algorithm to query subsequences within sentences, and propagate their labels to other sentences. We achieve highly efficient results on OntoNotes 5.0, only requiring 13% of the original training data, and CoNLL 2003, requiring only 27%. This is an improvement of 39% and 37% compared to querying full sentences.

show abstract

“…Moreover, the semi-supervised learning approach conquers the limitations of unsupervised and supervised approaches by using a supervised learning approach to make the finest prediction for unlabeled data by using the backpropagation algorithm unsupervised learning approach to find and learn the pattern in the inputs to make the finest prediction. This algorithm uses only a small set of labelled data [72]. Again, one of the widely adopted techniques of deep learning is transfer learning.…”

Section: Deep Learning-based Approaches For Sonar Automatic Target Recognitionmentioning

confidence: 99%

A Review on Deep Learning-Based Approaches for Automatic Sonar Target Recognition

Neupane

Seok

2020

Electronics

View full text Add to dashboard Cite

Underwater acoustics has been implemented mostly in the field of sound navigation and ranging (SONAR) procedures for submarine communication, the examination of maritime assets and environment surveying, target and object recognition, and measurement and study of acoustic sources in the underwater atmosphere. With the rapid development in science and technology, the advancement in sonar systems has increased, resulting in a decrement in underwater casualties. The sonar signal processing and automatic target recognition using sonar signals or imagery is itself a challenging process. Meanwhile, highly advanced data-driven machine-learning and deep learning-based methods are being implemented for acquiring several types of information from underwater sound data. This paper reviews the recent sonar automatic target recognition, tracking, or detection works using deep learning algorithms. A thorough study of the available works is done, and the operating procedure, results, and other necessary details regarding the data acquisition process, the dataset used, and the information regarding hyper-parameters is presented in this article. This paper will be of great assistance for upcoming scholars to start their work on sonar automatic target recognition.

show abstract

Semi-supervised learning: a brief review

Cited by 90 publications

References 10 publications

Data-Driven Fault Diagnosis for Electric Drives: A Review

Data-Driven Fault Diagnosis for Electric Drives: A Review

Subsequence Based Deep Active Learning for Named Entity Recognition

A Review on Deep Learning-Based Approaches for Automatic Sonar Target Recognition

Contact Info

Product

Resources

About