Modular deep learning algorithms for object classifications

Artificial intelligence is a kind of technology that simulates human intelligence. It uses computer programs to imitate human thinking and behavior to solve various tasks like classification and regression. AI can be divided into many different subfields, including ML, NLP, CV and reinforcement l...

Full description

Saved in:
Bibliographic Details
Main Author: Sun, Yansong
Other Authors: Cheah Chien Chern
Format: Thesis-Master by Coursework
Language:English
Published: Nanyang Technological University 2023
Subjects:
Online Access:https://hdl.handle.net/10356/169185
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-169185
record_format dspace
spelling sg-ntu-dr.10356-1691852023-07-08T05:40:10Z Modular deep learning algorithms for object classifications Sun, Yansong Cheah Chien Chern School of Electrical and Electronic Engineering ECCCheah@ntu.edu.sg Engineering::Electrical and electronic engineering::Computer hardware, software and systems Artificial intelligence is a kind of technology that simulates human intelligence. It uses computer programs to imitate human thinking and behavior to solve various tasks like classification and regression. AI can be divided into many different subfields, including ML, NLP, CV and reinforcement learning, among others. Among them, machine learning is among the core technologies in the field of AI, which uses algorithms and statistical methods to enable comput ers to automatically learn and improve from data. Modular Progressive learning is a machine learning technique in which a learning task is broken down into multiple subtasks and trained module by module using a series of models, each of which is responsible for solving a different subtask. In the training of each module, the model improves its performance by learning the output of the pre vious module’s model, and outputs it to the next layer’s model for training. Finally, the output of the entire divided model is combined to produce the fi nal result. Modular training techniques can help machine learning models learn complex features and patterns from large amounts of data and achieve supe rior performance in a variety of tasks and problems. Many fields and industries have witnessed the wide utility, including natural language processing, computer vision and speech recognition. However, modular training also has its limita tions, including the need for a large amount of computing resources and time, as well as the sensitivity to the selection of model architecture and hyperpa rameters. Therefore, model design and training strategies need to be carefully considered when using modular training techniques. Master of Science (Computer Control and Automation) 2023-07-05T06:20:14Z 2023-07-05T06:20:14Z 2023 Thesis-Master by Coursework Sun, Y. (2023). Modular deep learning algorithms for object classifications. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/169185 https://hdl.handle.net/10356/169185 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering::Computer hardware, software and systems
spellingShingle Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Sun, Yansong
Modular deep learning algorithms for object classifications
description Artificial intelligence is a kind of technology that simulates human intelligence. It uses computer programs to imitate human thinking and behavior to solve various tasks like classification and regression. AI can be divided into many different subfields, including ML, NLP, CV and reinforcement learning, among others. Among them, machine learning is among the core technologies in the field of AI, which uses algorithms and statistical methods to enable comput ers to automatically learn and improve from data. Modular Progressive learning is a machine learning technique in which a learning task is broken down into multiple subtasks and trained module by module using a series of models, each of which is responsible for solving a different subtask. In the training of each module, the model improves its performance by learning the output of the pre vious module’s model, and outputs it to the next layer’s model for training. Finally, the output of the entire divided model is combined to produce the fi nal result. Modular training techniques can help machine learning models learn complex features and patterns from large amounts of data and achieve supe rior performance in a variety of tasks and problems. Many fields and industries have witnessed the wide utility, including natural language processing, computer vision and speech recognition. However, modular training also has its limita tions, including the need for a large amount of computing resources and time, as well as the sensitivity to the selection of model architecture and hyperpa rameters. Therefore, model design and training strategies need to be carefully considered when using modular training techniques.
author2 Cheah Chien Chern
author_facet Cheah Chien Chern
Sun, Yansong
format Thesis-Master by Coursework
author Sun, Yansong
author_sort Sun, Yansong
title Modular deep learning algorithms for object classifications
title_short Modular deep learning algorithms for object classifications
title_full Modular deep learning algorithms for object classifications
title_fullStr Modular deep learning algorithms for object classifications
title_full_unstemmed Modular deep learning algorithms for object classifications
title_sort modular deep learning algorithms for object classifications
publisher Nanyang Technological University
publishDate 2023
url https://hdl.handle.net/10356/169185
_version_ 1772826299295858688