Deep neural networks (DNNs) have shown to perform very well on large scale object recognition problems and lead to widespread use for real-world applications, including situations where DNN are implemented as “black boxes”. A promising approach to secure their use is to accept decisions that are likely to be correct while discarding the others. In this work, we propose DOCTOR, a simple method that aims to identify whether the prediction of a DNN classifier should (or should not) be trusted so that, consequently, it would be possible to accept it or to reject it. Two scenarios are investigated: Totally Black Box (TBB) where only the soft-predictions are available and Partially Black Box (PBB) where gradient-propagation to perform input pre-processing is allowed. Empirically, we show that DOCTOR outperforms all state-of-the-art methods on various well-known images and sentiment analysis datasets. In particular, we observe a reduction of up to 4% of the false rejection rate (FRR) in the PBB scenario. DOCTOR can be applied to any pre-trained model, it does not require prior information about the underlying dataset and is as simple as the simplest available methods in the literature.

DOCTOR: A Simple Method for Detecting Misclassification Errors / Granese, Federica; Romanelli, Marco; Gorla, Daniele; Palamidessi, Catuscia; Piantanida, Pablo. - (2021). (Intervento presentato al convegno 35th Conference on Neural Information Processing Systems (NeurIPS 2021) tenutosi a Sydney (Australia)).

DOCTOR: A Simple Method for Detecting Misclassification Errors

Federica Granese;Daniele Gorla;
2021

Abstract

Deep neural networks (DNNs) have shown to perform very well on large scale object recognition problems and lead to widespread use for real-world applications, including situations where DNN are implemented as “black boxes”. A promising approach to secure their use is to accept decisions that are likely to be correct while discarding the others. In this work, we propose DOCTOR, a simple method that aims to identify whether the prediction of a DNN classifier should (or should not) be trusted so that, consequently, it would be possible to accept it or to reject it. Two scenarios are investigated: Totally Black Box (TBB) where only the soft-predictions are available and Partially Black Box (PBB) where gradient-propagation to perform input pre-processing is allowed. Empirically, we show that DOCTOR outperforms all state-of-the-art methods on various well-known images and sentiment analysis datasets. In particular, we observe a reduction of up to 4% of the false rejection rate (FRR) in the PBB scenario. DOCTOR can be applied to any pre-trained model, it does not require prior information about the underlying dataset and is as simple as the simplest available methods in the literature.
2021
35th Conference on Neural Information Processing Systems (NeurIPS 2021)
Miscalssification, Black-box prediction, Machine Learning
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
DOCTOR: A Simple Method for Detecting Misclassification Errors / Granese, Federica; Romanelli, Marco; Gorla, Daniele; Palamidessi, Catuscia; Piantanida, Pablo. - (2021). (Intervento presentato al convegno 35th Conference on Neural Information Processing Systems (NeurIPS 2021) tenutosi a Sydney (Australia)).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1572393
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