Exploring neural network interpretability in visual understanding
department or school: Applied Science, Faculty of
department or school: Electrical and Computer Engineering, Department of
data provider: DSpace
degree campus: UBCV
creator: Wang, Dan
issued: 2023-04-05T15:49:00Z
issued: 2023
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: Neural networks (NNs) have reached remarkable performance in computer vision. However, numerous parameters and complex structures make NNs opaque to humans. The failure to comprehend NNs may raise serious issues in real-world applications. My research aims to explore the NN interpretability in diverse visual tasks from post-hoc explanation and intrinsic interpretability perspectives. Convolutional neural networks (CNNs) have outperformed humans in image classification. However, the logic of network decisions remains a puzzle. As such, we propose concept-harmonized hierarchical inference, a post-hoc explanation framework, to explain the decision-making process of CNNs. Firstly, we interpret layered feature representations of NNs with hierarchical visual semantics. Then we explain the NN feature learning as a bottom-up decision logic from low to high semantic levels in which a deep-layer decision is decomposed as a sequence of shallow-layer sub-decisions. With the evolution of virtual reality, researchers are focusing increasingly on inverse rendering: reconstructing a 3D scene from multi-view 2D images. In this field, NNs achieved superior performance in novel view synthesis and 3D reconstruction. For both tasks, learning a 3D representation from input views is the key process where prior methods separately designed a CNN-based single-view feature extraction and a pooling-based multi-view fusion. This incoherent design damages their intrinsic interpretability and performance. Therefore, we aim to design coherent, interpretable NNs that can adequately exploit knowledge of relationships from data. For novel view synthesis, we propose a unified Transformer-based neural radiance field (TransNeRF) conditioned on source views to learn a generic 3D-scene representation. TransNeRF explores deep relationships between the target-rendering view and source views. TransNeRF also improves intrinsic interpretability by enhancing the shape and appearance consistency of a 3D scene. I…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/84143
note: Exploring Neural Network Interpretability in VisualUnderstandingbyDan WangB.Sc, Beihang University, 2014M.Sc, Beihang University, 2017A THESIS SUBMITTED IN PARTIAL FULFILLMENTOF THE REQUIREMENTS FOR THE DEGREE OFDoctor of PhilosophyinTHE FACULTY OF GRADUATE AND POSTDOCTORALSTUDIES(Electrical and Computer Engineering)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)April 2023© Dan Wang, 2023The following individuals certify that they have read, and recommend to the Fac-ulty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled:Exploring Neural Network Interpretability in Visual Understandingsubmitted by Dan Wang in partial fulfillment of the requirements for the degree ofDoctor of Philosophy in Electrical and Computer Engineering.Examining Committee:Z. Jane Wang, Professor, Electrical and Computer Engineering, UBCSupervisorSeptimiu Salcudean, Professor, Electrical and Computer Engineering, UBCCo-SupervisorRabab Ward, Professor, Electrical and Computer Engineering, UBCSupervisory Committee MemberPanos Nasiopoulos, Professor, Electrical and Computer Engineering, UBCUniversity ExaminerMatı´as Salibia´n-Barrera, Professor, Statistics, UBCUniversity ExamineriiAbstractNeural networks (NNs) have reached remarkable performance in computer vision.However, numerous parameters and complex structures make NNs opaque to hu-mans. The failure to comprehend NNs may raise serious issues in real-world ap-plications. My research aims to explore the NN interpretability in diverse visualtasks from post-hoc explanation and intrinsic interpretability perspectives.Convolutional neural networks (CNNs) have outperformed humans in imageclassification. However, the logic of network decisions remains a puzzle. Assuch, we propose concept-harmonized hierarchical inference, a post-hoc expla-nation framework, to explain the decision-making process of CNNs. Firstly, weinterpret layered feature representations of NNs with hierarchical visual seman-tics. Then we explain the NN feature le…
has type: Thesis/Dissertation
date issued: 2023-05
is shown at: 10.14288/1.0429922
language: eng
degree discipline: Electrical and Computer Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Wang, Z. Jane
contributor: Salcudean, Septimiu Edmund
type: Text
identifier URI: http://hdl.handle.net/2429/84143