Abstract
In the last half-decade, a new renaissance of machine learning originates from the applications of convolutional neural networks to visual recognition tasks. It is believed that a combination of big curated data and novel deep learning techniques can lead to unprecedented results. However, the increasingly large training data is still a drop in the ocean compared with scenarios in the wild. In this literature, we focus on learning transferable representation in the neural networks to ensure the models stay robust, even given different data distributions. We present three exemplar topics in three chapters, respectively: zero-shot learning, domain adaptation, and generalizable adversarial attack. By zero-shot learning, we enable models to predict labels not seen in the training phase. By domain adaptation, we improve a model's performance on the target domain by mitigating its discrepancy from a labeled source model, without any target annotation. Finally, the generalization adversarial attack focuses on learning an adversarial camouflage that ideally would work in every possible scenario. Despite sharing the same transfer learning philosophy, each of the proposed topics poses a unique challenge requiring a unique solution. In each chapter, we introduce the problem as well as present our solution to the problem. We also discuss some other researchers' approaches and compare our solution to theirs in the experiments.
Notes
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Graduation Date
2020
Semester
Spring
Advisor
Foroosh, Hassan
Degree
Doctor of Philosophy (Ph.D.)
College
College of Engineering and Computer Science
Department
Computer Science
Degree Program
Computer Science
Format
application/pdf
Identifier
CFE0008068; DP0023207
URL
https://purls.library.ucf.edu/go/DP0023207
Language
English
Release Date
May 2020
Length of Campus-only Access
None
Access Status
Doctoral Dissertation (Open Access)
STARS Citation
Zhang, Yang, "Learning Transferable Representations for Visual Recognition" (2020). Electronic Theses and Dissertations, 2020-. 162.
https://stars.library.ucf.edu/etd2020/162