Background subtraction using a pixel-wise adaptive learning rate for object tracking initialization

Adaptive learning rate and target re-detection for object tracking based on correlation filter

Advances in Optoelectronic Technology and Industry Development ◽

10.1201/9780429283628-2 ◽

2019 ◽

pp. 13-19

Author(s):

Jianhong Xiang ◽

Pengyu Shen

Keyword(s):

Object Tracking ◽

Adaptive Learning ◽

Learning Rate ◽

Correlation Filter ◽

Adaptive Learning Rate

Adaptive Learning Rate for Dealing with Imbalanced Data in Classification Problems

2021 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunication Engineering ◽

10.1109/ectidamtncon51128.2021.9425715 ◽

2021 ◽

Author(s):

Ratanon Jantanasukon ◽

Arit Thammano

Keyword(s):

Adaptive Learning ◽

Imbalanced Data ◽

Learning Rate ◽

Classification Problems ◽

Adaptive Learning Rate

Chinese News Text Classification of the Stacked Denoising Auto Encoder Based on Adaptive Learning Rate and Additional Momentum Item

Advances in Neural Networks – ISNN 2018 - Lecture Notes in Computer Science ◽

10.1007/978-3-319-92537-0_66 ◽

2018 ◽

pp. 578-584

Author(s):

Shuang Qiu ◽

Mingyang Jiang ◽

Zhifeng Zhang ◽

Yinan Lu ◽

Zhili Pei

Keyword(s):

Text Classification ◽

Adaptive Learning ◽

Learning Rate ◽

Adaptive Learning Rate

A coded block neural network system suitable for VLSI implementation using an adaptive learning-rate epoch-based back propagation technique

1993 IEEE International Symposium on Circuits and Systems ◽

10.1109/iscas.1993.394137 ◽

2002 ◽

Author(s):

M.W. Mao ◽

B.Y. Chen ◽

J.B. Kuo

Keyword(s):

Neural Network ◽

Adaptive Learning ◽

Back Propagation ◽

Learning Rate ◽

Vlsi Implementation ◽

Network System ◽

Adaptive Learning Rate ◽

Neural Network System ◽

Propagation Technique

Performance-Based Adaptive Learning Rate Scheduler Algorithm

Algorithms for Intelligent Systems - Applications of Artificial Intelligence in Engineering ◽

10.1007/978-981-33-4604-8_34 ◽

2021 ◽

pp. 417-433

Author(s):

Vakada Naveen ◽

Yaswanth Mareedu ◽

Neeharika Sai Mandava ◽

Sravya Kaveti ◽

G. Krishna Kishore

Keyword(s):

Adaptive Learning ◽

Learning Rate ◽

Adaptive Learning Rate

Adaptive Learning Rate-Based Convolutional Neural Network Models for Brain Tumor Images Classification

Mathematical Modelling and Computational Intelligence Techniques - Springer Proceedings in Mathematics & Statistics ◽

10.1007/978-981-16-6018-4_9 ◽

2021 ◽

pp. 135-146

Author(s):

Thiyagarajan Padmapriya ◽

Thiruvenkatam Kalaiselvi ◽

Karuppanagounder Somasundaram

Keyword(s):

Neural Network ◽

Brain Tumor ◽

Convolutional Neural Network ◽

Adaptive Learning ◽

Network Models ◽

Learning Rate ◽

Neural Network Models ◽

Adaptive Learning Rate

Correlation filter tracking based on adaptive learning rate and location refiner

Optics and Precision Engineering ◽

10.3788/ope.20182608.2100 ◽

2018 ◽

Vol 26 (8) ◽

pp. 2100-2111 ◽

Cited By ~ 1

Author(s):

刘教民 LIU Jiao-min ◽

郭剑威 GUO Jian-wei ◽

师硕 SHI Shuo

Keyword(s):

Adaptive Learning ◽

Learning Rate ◽

Correlation Filter ◽

Adaptive Learning Rate

MEMS Inertial Sensor Fault Diagnosis Using a CNN-Based Data-Driven Method

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s021800142059048x ◽

2020 ◽

Vol 34 (14) ◽

pp. 2059048

Author(s):

Tong Gao ◽

Wei Sheng ◽

Mingliang Zhou ◽

Bin Fang ◽

Liping Zheng

Keyword(s):

Fault Diagnosis ◽

Adaptive Learning ◽

Inertial Sensors ◽

Inertial Sensor ◽

Optimization Method ◽

Learning Rate ◽

Data Driven ◽

Adaptive Learning Rate ◽

Improved Performance ◽

Rate Optimization

In this paper, we propose a novel fault diagnosis (FD) approach for micro-electromechanical systems (MEMS) inertial sensors that recognize the fault patterns of MEMS inertial sensors in an end-to-end manner. We use a convolutional neural network (CNN)-based data-driven method to classify the temperature-related sensor faults in unmanned aerial vehicles (UAVs). First, we formulate the FD problem for MEMS inertial sensors into a deep learning framework. Second, we design a multi-scale CNN which uses the raw data of MEMS inertial sensors as input and which outputs classification results indicating faults. Then we extract fault features in the temperature domain to solve the non-uniform sampling problem. Finally, we propose an improved adaptive learning rate optimization method which accelerates the loss convergence by using the Kalman filter (KF) to train the network efficiently with a small dataset. Our experimental results show that our method achieved high fault recognition accuracy and that our proposed adaptive learning rate method improved performance in terms of loss convergence and robustness on a small training batch.

Time Series Prediction for Kernel-based Adaptive Filters Using Variable Bandwidth, Adaptive Learning-rate, and Dimensionality Reduction

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ◽

10.1109/icassp.2019.8683117 ◽

2019 ◽

Author(s):

S. Garcia-Vega ◽

E. A. Leon-Gomez ◽

G. Castellanos-Dominguez

Keyword(s):

Time Series ◽

Dimensionality Reduction ◽

Adaptive Learning ◽

Adaptive Filters ◽

Time Series Prediction ◽

Learning Rate ◽

Adaptive Learning Rate ◽

Variable Bandwidth

Adaptive Learning Rate Tuning Algorithm for RF Self-Interference Cancellation

IEEE Transactions on Microwave Theory and Techniques ◽

10.1109/tmtt.2020.3021100 ◽

2020 ◽

pp. 1-1

Author(s):

Kenneth E. Kolodziej ◽

Aidan U. Cookson ◽

Bradley T. Perry

Keyword(s):

Adaptive Learning ◽

Interference Cancellation ◽

Learning Rate ◽

Adaptive Learning Rate