Uncertainty-aware deep classifiers using generative models

Name: Uncertainty-aware deep classifiers using generative models
Author: Şensoy, Murat, Kaplan, L., Cerutti, F., Saleki, Maryam

İsim	Uncertainty-aware deep classifiers using generative models
Yazar	Şensoy, Murat, Kaplan, L., Cerutti, F., Saleki, Maryam
Basım Tarihi:	2020
Basım Yeri	- Association for the Advancement of Artificial Intelligence
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-157735835-0
Kayıt Numarası	ba404e69-fa0e-41e2-8d88-4cc38649ede4
Lokasyon	Computer Science
Tarih	2020
Notlar	United States Department of Defense US Army Research Laboratory (ARL) ; U.K. Ministry of Defence ; Newton-Katip Celebi Fund ; TÜBİTAK
Örnek Metin	Deep neural networks are often ignorant about what they do not know and overconfident when they make uninformed predictions. Some recent approaches quantify classification uncertainty directly by training the model to output high uncertainty for the data samples close to class boundaries or from the outside of the training distribution. These approaches use an auxiliary data set during training to represent out-of-distribution samples. However, selection or creation of such an auxiliary data set is non-trivial, especially for high dimensional data such as images. In this work we develop a novel neural network model that is able to express both aleatoric and epistemic uncertainty to distinguish decision boundary and out-of-distribution regions of the feature space. To this end, variational autoencoders and generative adversarial networks are incorporated to automatically generate out-of-distribution exemplars for training. Through extensive analysis, we demonstrate that the proposed approach provides better estimates of uncertainty for in- and out-of-distribution samples, and adversarial examples on well-known data sets against state-of-the-art approaches including recent Bayesian approaches for neural networks and anomaly detection methods.
Cilt	34

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Uncertainty-aware deep classifiers using generative models

Yazar Şensoy, Murat, Kaplan, L., Cerutti, F., Saleki, Maryam

Basım Tarihi 2020

Basım Yeri - Association for the Advancement of Artificial Intelligence

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-157735835-0

Kayıt Numarası ba404e69-fa0e-41e2-8d88-4cc38649ede4

Lokasyon Computer Science

Tarih 2020

Notlar United States Department of Defense US Army Research Laboratory (ARL) ; U.K. Ministry of Defence ; Newton-Katip Celebi Fund ; TÜBİTAK

Örnek Metin Deep neural networks are often ignorant about what they do not know and overconfident when they make uninformed predictions. Some recent approaches quantify classification uncertainty directly by training the model to output high uncertainty for the data samples close to class boundaries or from the outside of the training distribution. These approaches use an auxiliary data set during training to represent out-of-distribution samples. However, selection or creation of such an auxiliary data set is non-trivial, especially for high dimensional data such as images. In this work we develop a novel neural network model that is able to express both aleatoric and epistemic uncertainty to distinguish decision boundary and out-of-distribution regions of the feature space. To this end, variational autoencoders and generative adversarial networks are incorporated to automatically generate out-of-distribution exemplars for training. Through extensive analysis, we demonstrate that the proposed approach provides better estimates of uncertainty for in- and out-of-distribution samples, and adversarial examples on well-known data sets against state-of-the-art approaches including recent Bayesian approaches for neural networks and anomaly detection methods.

Cilt 34