From Macro to Micro Expression Recognition: Deep Learning on Small Datasets Using Transfer Learning

2018 ◽  
Author(s):  
Min Peng ◽  
Zhan Wu ◽  
Zhihao Zhang ◽  
Tong Chen
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Expression Recognition ◽  
Micro Expression

Comparative Analysis of Micro Expression Recognition using Deep Learning and Transfer Learning

2021 ◽  
Author(s):  
Rahil Kadakia ◽  
Parth Kalkotwar ◽  
Pruthav Jhaveri ◽  
Rahul Patanwadia ◽  
Kriti Srivastava
Keyword(s):  
Deep Learning ◽  
Comparative Analysis ◽  
Transfer Learning ◽  
Expression Recognition ◽  
Micro Expression

Transfer learning for micro-expression recognition based on the difference key frame images

2021 ◽  
Author(s):  
Zhihua Xie ◽  
Le Wang ◽  
Ling Shi ◽  
Jiawei Fan ◽  
sijia Cheng
Keyword(s):  
Transfer Learning ◽  
Expression Recognition ◽  
Key Frame ◽  
The Difference ◽  
Micro Expression

Deep Learning Based Transfer Learning for Possible Facial Psychological Expression Recognition

2018 ◽  
Vol 8 (7) ◽  
pp. 1478-1485
Author(s):  
Mi Li ◽  
Lei Cao ◽  
Dachao Liu ◽  
Leilei Li ◽  
Shengfu Lu
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Expression Recognition

Facial micro-expression recognition based on the fusion of deep learning and enhanced optical flow

2018 ◽  
Vol 78 (20) ◽  
pp. 29307-29322 ◽  
Author(s):  
Qiuyu Li ◽  
Shu Zhan ◽  
Liangfeng Xu ◽  
Congzhong Wu
Keyword(s):  
Deep Learning ◽  
Optical Flow ◽  
Expression Recognition ◽  
Micro Expression

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

scholarly journals Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4736
Author(s):  
Sk. Tanzir Mehedi ◽  
Adnan Anwar ◽  
Ziaur Rahman ◽  
Kawsar Ahmed
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Real Time ◽  
Transfer Learning ◽  
Security Requirements ◽  
Detection Accuracy ◽  
Area Network ◽  
Complex Data ◽  
Network Intrusion

The Controller Area Network (CAN) bus works as an important protocol in the real-time In-Vehicle Network (IVN) systems for its simple, suitable, and robust architecture. The risk of IVN devices has still been insecure and vulnerable due to the complex data-intensive architectures which greatly increase the accessibility to unauthorized networks and the possibility of various types of cyberattacks. Therefore, the detection of cyberattacks in IVN devices has become a growing interest. With the rapid development of IVNs and evolving threat types, the traditional machine learning-based IDS has to update to cope with the security requirements of the current environment. Nowadays, the progression of deep learning, deep transfer learning, and its impactful outcome in several areas has guided as an effective solution for network intrusion detection. This manuscript proposes a deep transfer learning-based IDS model for IVN along with improved performance in comparison to several other existing models. The unique contributions include effective attribute selection which is best suited to identify malicious CAN messages and accurately detect the normal and abnormal activities, designing a deep transfer learning-based LeNet model, and evaluating considering real-world data. To this end, an extensive experimental performance evaluation has been conducted. The architecture along with empirical analyses shows that the proposed IDS greatly improves the detection accuracy over the mainstream machine learning, deep learning, and benchmark deep transfer learning models and has demonstrated better performance for real-time IVN security.

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