Self-Correction Method for Automatic Data Annotation

2017 ◽  
Author(s):  
Ce Liu ◽  
Tonghua Su ◽  
Lijuan Yu
Keyword(s):  
Correction Method ◽  
Automatic Data ◽  
Data Annotation

scholarly journals DeepTrigger: A Watermarking Scheme of Deep Learning Models Based on Chaotic Automatic Data Annotation

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 213296-213305
Author(s):  
Ying-Qian Zhang ◽  
Yi-Ran Jia ◽  
Xingyuan Wang ◽  
Qiong Niu ◽  
Nian-Dong Chen
Keyword(s):  
Deep Learning ◽  
Learning Models ◽  
Automatic Data ◽  
Data Annotation ◽  
Watermarking Scheme

scholarly journals Semi-Automatic Data Annotation, POS Tagging and Mildly Context-Sensitive Disambiguation: The eXtended Revised AraMorph (XRAM)

2018 ◽  
pp. 155-168
Author(s):  
Giuliano Lancioni ◽  
Laura Garofalo ◽  
Raoul Villano ◽  
Francesca Romana Romani ◽  
Marta Campanelli ◽  
...  
Keyword(s):  
Automatic Data ◽  
Data Annotation ◽  
Context Sensitive ◽  
Pos Tagging

scholarly journals Semi-Automatic Data Annotation guided by Feature Space Projection

2020 ◽  
Author(s):  
Bárbara C. Benato ◽  
Alexandru C. Telea ◽  
Alexandre X. Falcão
Keyword(s):  
Visual Analytics ◽  
Visual Analysis ◽  
Intestinal Parasites ◽  
Feature Space ◽  
Training Sample ◽  
Added Value ◽  
Automatic Data ◽  
Data Annotation ◽  
Unseen Data ◽  
Space Projection

Data annotation using visual inspection (supervision) of each training sample can be laborious. Interactive solutions alleviate this by helping experts propagate labels from a few supervised samples to unlabeled ones based solely on the visual analysis of their feature space projection (with no further sample supervision). We present a semi-automatic data annotation approach based on suitable feature space projection and semi-supervised label estimation. We validate our method on the popular MNIST dataset and on images of human intestinal parasites with and without fecal impurities, a large and diverse dataset that makes classification very hard. We evaluate two approaches for semi-supervised learning from the latent and projection spaces, to choose the one that best reduces user annotation effort and also increases classification accuracy on unseen data. Our results demonstrate the added-value of visual analytics tools that combine complementary abilities of humans and machines for more effective machine learning.

scholarly journals Semi-automatic data annotation guided by feature space projection

2021 ◽  
Vol 109 ◽  
pp. 107612
Author(s):  
Bárbara C. Benato ◽  
Jancarlo F. Gomes ◽  
Alexandru C. Telea ◽  
Alexandre X. Falcão
Keyword(s):  
Feature Space ◽  
Automatic Data ◽  
Data Annotation ◽  
Space Projection

Proximity based automatic data annotation for autonomous driving

2020 ◽  
Vol 7 (2) ◽  
pp. 395-404 ◽  
Author(s):  
Chen Sun ◽  
Jean M. Uwabeza Vianney ◽  
Ying Li ◽  
Long Chen ◽  
Li Li ◽  
...  
Keyword(s):  
Autonomous Driving ◽  
Automatic Data ◽  
Data Annotation

Instrumentation For Quantitative Microscopy

1977 ◽  
Vol 35 ◽  
pp. 206-209
Author(s):  
B. Ralph ◽  
A.R. Jones
Keyword(s):  
Volume Fraction ◽  
Three Dimensional ◽  
Two Dimensional ◽  
Automatic Data ◽  
Phase Volume Fraction ◽  
Statistical Confidence ◽  
Resultant Data ◽  
Automatic Data Collection ◽  
Third Dimension ◽  
Evolution Of Microstructure

In all fields of microscopy there is an increasing interest in the quantification of microstructure. This interest may stem from a desire to establish quality control parameters or may have a more fundamental requirement involving the derivation of parameters which partially or completely define the three dimensional nature of the microstructure. This latter categorey of study may arise from an interest in the evolution of microstructure or from a desire to generate detailed property/microstructure relationships. In the more fundamental studies some convolution of two-dimensional data into the third dimension (stereological analysis) will be necessary.In some cases the two-dimensional data may be acquired relatively easily without recourse to automatic data collection and further, it may prove possible to perform the data reduction and analysis relatively easily. In such cases the only recourse to machines may well be in establishing the statistical confidence of the resultant data. Such relatively straightforward studies tend to result from acquiring data on the whole assemblage of features making up the microstructure. In this field data mode, when parameters such as phase volume fraction, mean size etc. are sought, the main case for resorting to automation is in order to perform repetitive analyses since each analysis is relatively easily performed.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques
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