Variational bayesian multiple instance learning with gaussian processes

Name: Variational bayesian multiple instance learning with gaussian processes
Author: Haussmann, M., Hamprecht, F. A., Kandemir, Melih

İsim	Variational bayesian multiple instance learning with gaussian processes
Yazar	Haussmann, M., Hamprecht, F. A., Kandemir, Melih
Basım Tarihi:	2017
Basım Yeri	- IEEE
Konu	Bayes methods, Cancer, Expectation-maximisation algorithm, Gaussian processes, Image classification, Learning (artificial intelligence), Object detection, Pattern classification, Tumours
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-1-5386-0457-1
Kayıt Numarası	fda540fa-58ee-4642-8ae2-e944e68ae256
Lokasyon	Computer Science
Tarih	2017
Notlar	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Örnek Metin	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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Variational bayesian multiple instance learning with gaussian processes

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

Basım Tarihi 2017

Basım Yeri - IEEE

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

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-1-5386-0457-1

Kayıt Numarası fda540fa-58ee-4642-8ae2-e944e68ae256

Lokasyon Computer Science

Tarih 2017

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

Örnek Metin 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