Applications of Sliding Window Attention
This sliding window attention approach has been widely used in a variety of research. A few of the research topics are mentioned below:
- Automatic Left Ventricle Detection System:
The research is done on MR cardiac images with the objective of creating an artificial vision model that performs automated localization of the Left Ventricle in the input images.
Link: https://downloads.hindawi.com/journals/acisc/2017/3048181.pdf
- Time Series Data Prediction Using Sliding Window
The research is based on next day close prediction of time series data based on the concept of sliding window and WMA as data preprocessing, 10-fold cross validation was used to train RBFN model with preprocessed data for accurate prediction.
Link: https://www.ripublication.com/ijcir17/ijcirv13n5_46.pdf
Sliding Window Attention
Sliding Window Attention is a type of attention mechanism used in neural networks. The attention mechanism allows the model to focus on different parts of the input sequence when making predictions, providing a more flexible and content-aware approach.
Prerequisite: Attention Mechanism | ML
A wise man once said, “Manage your attention, not your time and you’ll get things done faster”.
In this article, we will be covering all about the Sliding window attention mechanisms used in Deep Learning as well as the working of the classifier.