Monash University
Browse

Enhancing Uncertainty Estimation of Multimodal Deep Learning

Download (9.68 MB)
thesis
posted on 2025-03-17, 07:25 authored by Myong Chol Jung
This thesis presents novel methods for accurately measuring the uncertainty in predictions made by multimodal deep learning models. These models not only understand various data types, such as images and text, but also provide reliable uncertainty estimation. This allows the effective use of diverse data sources in real-world applications and makes deep learning models more trustworthy and reliable. The proposed methods have significant potential in safety-critical domains like medical diagnosis and autonomous driving, where integrating multiple data sources and ensuring model reliability are essential.

History

Campus location

Australia

Principal supervisor

Lan Du

Additional supervisor 1

He Zhao

Additional supervisor 2

Joanna Dipnall

Additional supervisor 3

Belinda Gabbe

Year of Award

2025

Department, School or Centre

Data Science & Artificial Intelligence

Course

Doctor of Philosophy

Degree Type

DOCTORATE

Faculty

Faculty of Information Technology

Usage metrics

    Faculty of Information Technology Theses

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC