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Variational bayesian multiple instance learning with gaussian processes

Title	Variational bayesian multiple instance learning with gaussian processes
Author	Haussmann, M., Hamprecht, F. A., Kandemir, Melih
Publication Date:	2017
Publication Place	- IEEE
Subject	Bayes methods, Cancer, Expectation-maximisation algorithm, Gaussian processes, Image classification, Learning (artificial intelligence), Object detection, Pattern classification, Tumours
Type	Document
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	978-1-5386-0457-1
Record ID	fda540fa-58ee-4642-8ae2-e944e68ae256
Library Location	Computer Science
Date	2017
Notes	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Sample Text	Gaussian Processes (GPs) are effective Bayesian predictors. We here show for the first time that instance labels of a GP classifier can be inferred in the multiple instance learning (MIL) setting using variational Bayes. We achieve this via a new construction of the bag likelihood that assumes a large value if the instance predictions obey the MIL constraints and a small value otherwise. This construction lets us derive the update rules for the variational parameters analytically, assuring both scalable learning and fast convergence. We observe this model to improve the state of the art in instance label prediction from bag-level supervision in the 20 Newsgroups benchmark, as well as in Barretts cancer tumor localization from histopathology tissue microarray images. Furthermore, we introduce a novel pipeline for weakly supervised object detection naturally complemented with our model, which improves the state of the art on the PASCAL VOC 2007 and 2012 data sets. Last but not least, the performance of our model can be further boosted up using mixed supervision: a combination of weak (bag) and strong (instance) labels.
DOI	10.1109/CVPR.2017.93

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Özyeğin University

Variational bayesian multiple instance learning with gaussian processes

Author Haussmann, M., Hamprecht, F. A., Kandemir, Melih

Publication Date 2017

Publication Place - IEEE

Subject Bayes methods, Cancer, Expectation-maximisation algorithm, Gaussian processes, Image classification, Learning (artificial intelligence), Object detection, Pattern classification, Tumours

Type Document

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 978-1-5386-0457-1

Record ID fda540fa-58ee-4642-8ae2-e944e68ae256

Library Location Computer Science

Date 2017

Notes Due to copyright restrictions, the access to the full text of this article is only available via subscription.

Sample Text Gaussian Processes (GPs) are effective Bayesian predictors. We here show for the first time that instance labels of a GP classifier can be inferred in the multiple instance learning (MIL) setting using variational Bayes. We achieve this via a new construction of the bag likelihood that assumes a large value if the instance predictions obey the MIL constraints and a small value otherwise. This construction lets us derive the update rules for the variational parameters analytically, assuring both scalable learning and fast convergence. We observe this model to improve the state of the art in instance label prediction from bag-level supervision in the 20 Newsgroups benchmark, as well as in Barretts cancer tumor localization from histopathology tissue microarray images. Furthermore, we introduce a novel pipeline for weakly supervised object detection naturally complemented with our model, which improves the state of the art on the PASCAL VOC 2007 and 2012 data sets. Last but not least, the performance of our model can be further boosted up using mixed supervision: a combination of weak (bag) and strong (instance) labels.

DOI 10.1109/CVPR.2017.93