Open Set Learning with Counterfactual Images

Graduate Thesis Or Dissertation

Public Deposited

Citeable URL: https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/pk02cg679

Descriptions

Attribute Name	Values
Creator	Neal, Lawrence W.
Abstract	In open set recognition, a classifier must label instances of known classes while detecting instances of unknown classes not encountered during training. To detect unknown classes while still generalizing to new instances of existing classes, this thesis introduces a dataset augmentation technique called counterfactual image generation. This approach, based on generative adversarial networks, generates synthetic examples that are close to training set examples yet do not belong to any training category. By augmenting the training data with examples generated by this optimization, we can reformulate open set recognition as classification with one additional class: a class consisting of set of novel and unknown examples. This approach outperforms existing open set recognition algorithms on a selection of image classification tasks.
License	All rights reserved
Resource Type	Masters Thesis
Date Issued	2018-11-20
Degree Level	Master's
Degree Name	Master of Science (M.S.)
Degree Field	Computer Science
Degree Grantor	Oregon State University
Commencement Year	2019
Advisor	Fern, Xiaoli Z. Li, Fuxin
Committee Member	Wong, Weng-Keen Fern, Alan
Academic Affiliation	Electrical Engineering and Computer Science
Rights Statement	In Copyright
Publisher	Oregon State University
Peer Reviewed	No
Language	English [eng]

Parents:

This work has no parents.

In Collection:

Thumbnail	Title	Date Uploaded	Visibility	Actions
	NealLawrenceW2018.pdf	2018-12-06	Public	Download