A Review on Oracle Issues in Machine Learning

Seca, Diogo

doi:10.48550/arxiv.2105.01407

Cited by 1 publication

(1 citation statement)

References 21 publications

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“…We emphasize true ambiguity to indicate ambiguity intrinsic to the data and independent from any model and its classification confidence/accuracy. In this way we distinguish ours from other papers which also use the term ambiguous with different meaning, such as low confidence inputs, mislabelled inputs, where a label in the training/test set is not consistent with the ground truth [24], or invalid inputs, where no true label exists for a given input 1 . In simple domains, where humans may have no epistemic uncertainty (i.e., they know the matter perfectly), true ambiguity is equivalent to human ambiguity.…”

Section: Introductionmentioning

confidence: 99%

A Forgotten Danger in DNN Supervision Testing: Generating and Detecting True Ambiguity

Weiss¹,

Gómez²,

Tonella³

2022

Preprint

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Deep Neural Networks (DNNs) are becoming a crucial component of modern software systems, but they are prone to fail under conditions that are different from the ones observed during training (out-of-distribution inputs) or on inputs that are truly ambiguous, i.e., inputs that admit multiple classes with nonzero probability in their ground truth labels. Recent work proposed DNN supervisors to detect highuncertainty inputs before their possible misclassification leads to any harm. To test and compare the capabilities of DNN supervisors, researchers proposed test generation techniques, to focus the testing effort on high-uncertainty inputs that should be recognized as anomalous by supervisors. However, existing test generators can only produce out-of-distribution inputs. No existing model-and supervisor independent technique supports the generation of truly ambiguous test inputs.In this paper, we propose a novel way to generate ambiguous inputs to test DNN supervisors and used it to empirically compare several existing supervisor techniques. In particular, we propose AMBIGUESS to generate ambiguous samples for image classification problems. AMBIGUESS is based on gradientguided sampling in the latent space of a regularized adversarial autoencoder. Moreover, we conducted what is -to the best of our knowledge -the most extensive comparative study of DNN supervisors, considering their capabilities to detect 4 distinct types of high-uncertainty inputs, including truly ambiguous ones.

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Section: Introductionmentioning

confidence: 99%