A Data Augmentation Model Based on Variational Approach

2018 ◽  
pp. 157-168
Author(s):  
Lei Xia ◽  
Jiancheng Lv ◽  
Yong Xu
Keyword(s):  
Variational Approach ◽  
Data Augmentation ◽  
Model Based

Generative Model based Data Augmentation for Special Person Classification

2020 ◽  
Author(s):  
Zijie Guo ◽  
Rong Zhi ◽  
Wuqaing Zhang ◽  
Baofeng Wang ◽  
Zhijie Fang ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Generative Model ◽  
Model Based ◽  
Special Person

The variational approach to the Glashow–Weinberg–Salam model

1987 ◽  
Vol 65 (4) ◽  
pp. 395-402 ◽  
Author(s):  
R. Manka ◽  
J. Sladkowski
Keyword(s):  
Phase Transition ◽  
Effective Potential ◽  
Variational Approach ◽  
Canonical Quantization ◽  
Temperature Dependent ◽  
Model Based

The variational approach to the Glashow–Weinberg–Salam model, based on canonical quantization, is presented. It is shown that taking into consideration the Becchi–Rouet–Stora symmetry leads to the correct, temperature-dependent, effective potential. This generalization of the Weinberg–Coleman potential leads to a phase transition of the first kind.

scholarly journals Improving Machine-Learning Diagnostics with Model-Based Data Augmentation Showcased for a Transformer Fault

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6816
Author(s):  
Jannis N. Kahlen ◽  
Michael Andres ◽  
Albert Moser
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
State Of The Art ◽  
Synthetic Data ◽  
Electrical Equipment ◽  
Diagnostic Systems ◽  
Additional Information ◽  
Model Based ◽  
Augmentation Techniques ◽  
Abnormal Condition

Machine-learning diagnostic systems are widely used to detect abnormal conditions in electrical equipment. Training robust and accurate diagnostic systems is challenging because only small databases of abnormal-condition data are available. However, the performance of the diagnostic systems depends on the quantity and quality of the data. The training database can be augmented utilizing data augmentation techniques that generate synthetic data to improve diagnostic performance. However, existing data augmentation techniques are generic methods that do not include additional information in the synthetic data. In this paper, we develop a model-based data augmentation technique integrating computer-implementable electromechanical models. Synthetic normal- and abnormal-condition data are generated with an electromechanical model and a stochastic parameter value sampling method. The model-based data augmentation is showcased to detect an abnormal condition of a distribution transformer. First, the synthetic data are compared with the measurements to verify the synthetic data. Then, ML-based diagnostic systems are created using model-based data augmentation and are compared with state-of-the-art diagnostic systems. It is shown that using the model-based data augmentation results in an improved accuracy compared to state-of-the-art diagnostic systems. This holds especially true when only a small abnormal-condition database is available.

Exploring Conditional Language Model Based Data Augmentation Approaches for Hate Speech Classification

2021 ◽  
pp. 135-146
Author(s):  
Ashwin Geet D’Sa ◽  
Irina Illina ◽  
Dominique Fohr ◽  
Dietrich Klakow ◽  
Dana Ruiter
Keyword(s):  
Data Augmentation ◽  
Hate Speech ◽  
Language Model ◽  
Model Based ◽  
Speech Classification

scholarly journals A Postearthquake Multiple Scene Recognition Model Based on Classical SSD Method and Transfer Learning

2020 ◽  
Vol 9 (4) ◽  
pp. 238 ◽  
Author(s):  
Zhiqiang Xu ◽  
Yumin Chen ◽  
Fan Yang ◽  
Tianyou Chu ◽  
Hongyan Zhou
Keyword(s):  
Transfer Learning ◽  
Data Augmentation ◽  
Recognition Performance ◽  
Scene Recognition ◽  
Support Vector ◽  
Detection Accuracy ◽  
Single Shot ◽  
Visual Interpretation ◽  
Model Based ◽  
Original Dataset

The recognition of postearthquake scenes plays an important role in postearthquake rescue and reconstruction. To overcome the over-reliance on expert visual interpretation and the poor recognition performance of traditional machine learning in postearthquake scene recognition, this paper proposes a postearthquake multiple scene recognition (PEMSR) model based on the classical deep learning Single Shot MultiBox Detector (SSD) method. In this paper, a labeled postearthquake scenes dataset is constructed by segmenting acquired remote sensing images, which are classified into six categories: landslide, houses, ruins, trees, clogged and ponding. Due to the insufficiency and imbalance of the original dataset, transfer learning and a data augmentation and balancing strategy are utilized in the PEMSR model. To evaluate the PEMSR model, the evaluation metrics of precision, recall and F1 score are used in the experiment. Multiple experimental test results demonstrate that the PEMSR model shows a stronger performance in postearthquake scene recognition. The PEMSR model improves the detection accuracy of each scene compared with SSD by transfer learning and data augmentation strategy. In addition, the average detection time of the PEMSR model only needs 0.4565s, which is far less than the 8.3472s of the traditional Histogram of Oriented Gradient + Support Vector Machine (HOG+SVM) method.

Sign in / Sign up

Export Citation Format

Share Document