Attention Mechanism in Machine Learning
Over the years, attention mechanism is becoming increasingly popular in machine learning. Attention is the ability to choose, concentrate and process relevant stimuli. The concept has been studied across different disciplines such as neuroscience and psychology. Although the disciplines may have varying definitions of attention, they all agree it helps make neural systems more flexible. The attention mechanism is a revolutionary concept introduced to enhance the performance and interpretability of the encoder and decoder framework for machine translation. That has changed the way we work with and apply deep learning algorithms. This article will discuss the specific applications of the attention mechanism in machine learning.
Application of Attention mechanism in different fields of Machine learning
Attention mechanism has frequently been incorporated in areas that involve processing sequences. Machine learning involves working with an artificial neural network, and the concept makes the system more flexible. Below are some of the major applications of attention in different fields of machine learning.
#1: Attention in Natural Language Processing (NLP)
Natural Language Processing, abbreviated NLP is one of the architectures of neural networks. NLP enables computers to comprehend natural language as humans do. The computer has artificial intelligence that inputs and processes written or spoken language in a manner the computer understands. The attention mechanism in NLP helps in highlighting relevant features from the input data.
By utilizing attention in NLP, developers can structure and organize knowledge to carry out various tasks such as sentiment analysis, automatic summarization, speech recognition, topic segmentation, and more.
#2: Attention for Visual Tasks
Like in psychology and neuroscience, most machine learning involves visual tasks. Attention has several applications in computer vision, from image captioning and classification to image segmentation.
Different attention-based tools such as saliency map aid while graphic work on the computer. The convolutional neural networks help improve performance in various functions, including image-inspired NLP. Visual tasks’ attention is categorized into two; spatial and feature-based attention. Spatial visual attention allows humans to grant priority in selectively processing visual information. At the same time, feature-based attention improves the representation of image features across the visual field.
#3: Multi-Task Attention
One of the most challenging topics in machine learning is multi-task learning. It involves obliging one network to perform more than one task simultaneously. However, training a network to perform a couple of functions can be tricky because the tasks may contradict each other regarding the attention needed. Multi-task attention has been successfully used in many machine learning applications, from NLP and computer vision to speech recognition.
#4: Attention to Memory
Usually, memory has limited space, so attention is the determining factor in what will be encoded. Through training, neural networks learn how to appropriately interact with the memory stored as a group of vectors to perform tasks such as sorting the reserved sequence. The process of expediting interaction is one form of interaction. The vector with important weight in terms of a word in a sentence and pixels in an image is the attention in the neural network.
Attention is a great mechanism that helps enhance the performance of the encode and decode architecture on neural networks. Although the attention mechanism was initially designed for Neural network translations, it also has other applications in other fields of machine learning from Natural Language Processing, visual tasks, multi-task, and memory. Attention in NLP enables the neural network to outline the relevant information from the input data dynamically. Also, visual task attention help in several applications, from image classification and captioning to image segmentation. At the same time, attention in memory helps the neural network to focus on the weighted vector.
This article is a summary of Lindsay (2020) chapter, in: Attention in Psychology, Neuroscience, and Machine Learning (DOI: https://doi.org/10.3389/fncom.2020.00029)