A Survey of Deep Active Learning

08/30/2020
by   Pengzhen Ren, et al.
148

Active learning (AL) attempts to maximize the performance gain of the model by marking the fewest samples. Deep learning (DL) is greedy for data and requires a large amount of data supply to optimize massive parameters, so that the model learns how to extract high-quality features. In recent years, due to the rapid development of internet technology, we are in an era of information torrents and we have massive amounts of data. In this way, DL has aroused strong interest of researchers and has been rapidly developed. Compared with DL, researchers have relatively low interest in AL. This is mainly because before the rise of DL, traditional machine learning requires relatively few labeled samples. Therefore, early AL is difficult to reflect the value it deserves. Although DL has made breakthroughs in various fields, most of this success is due to the publicity of the large number of existing annotation datasets. However, the acquisition of a large number of high-quality annotated datasets consumes a lot of manpower, which is not allowed in some fields that require high expertise, especially in the fields of speech recognition, information extraction, medical images, etc. Therefore, AL has gradually received due attention. A natural idea is whether AL can be used to reduce the cost of sample annotations, while retaining the powerful learning capabilities of DL. Therefore, deep active learning (DAL) has emerged. Although the related research has been quite abundant, it lacks a comprehensive survey of DAL. This article is to fill this gap, we provide a formal classification method for the existing work, and a comprehensive and systematic overview. In addition, we also analyzed and summarized the development of DAL from the perspective of application. Finally, we discussed the confusion and problems in DAL, and gave some possible development directions for DAL.

READ FULL TEXT
research
03/25/2022

A Comparative Survey of Deep Active Learning

Active Learning (AL) is a set of techniques for reducing labeling cost b...
research
12/15/2021

Assisted Text Annotation Using Active Learning to Achieve High Quality with Little Effort

Large amounts of annotated data have become more important than ever, es...
research
07/08/2023

Active Learning in Physics: From 101, to Progress, and Perspective

Active Learning (AL) is a family of machine learning (ML) algorithms tha...
research
11/26/2021

Active Learning for Event Extraction with Memory-based Loss Prediction Model

Event extraction (EE) plays an important role in many industrial applica...
research
08/23/2022

Survey on Evolutionary Deep Learning: Principles, Algorithms, Applications and Open Issues

Over recent years, there has been a rapid development of deep learning (...
research
01/15/2021

Hyperspectral Image Classification – Traditional to Deep Models: A Survey for Future Prospects

Hyperspectral Imaging (HSI) has been extensively utilized in many real-l...
research
06/12/2023

A Survey of Modern Compiler Fuzzing

Most software that runs on computers undergoes processing by compilers. ...

Please sign up or login with your details

Forgot password? Click here to reset