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Black-box error diagnosis in Deep Neural Networks for computer vision: a survey of tools

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Abstract

The application of Deep Neural Networks (DNNs) to a broad variety of tasks demands methods for coping with the complex and opaque nature of these architectures. When a gold standard is available, performance assessment treats the DNN as a black box and computes standard metrics based on the comparison of the predictions with the ground truth. A deeper understanding of performances requires going beyond such evaluation metrics to diagnose the model behavior and the prediction errors. This goal can be pursued in two complementary ways. On one side, model interpretation techniques “open the box” and assess the relationship between the input, the inner layers and the output, so as to identify the architecture modules most likely to cause the performance loss. On the other hand, black-box error diagnosis techniques study the correlation between the model response and some properties of the input not used for training, so as to identify the features of the inputs that make the model fail. Both approaches give hints on how to improve the architecture and/or the training process. This paper focuses on the application of DNNs to computer vision (CV) tasks and presents a survey of the tools that support the black-box performance diagnosis paradigm. It illustrates the features and gaps of the current proposals, discusses the relevant research directions and provides a brief overview of the diagnosis tools in sectors other than CV.

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Data availability

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

Notes

  1. The link to the code repository is navigable in the online version of the paper.

  2. https://blackboxnlp.github.io/.

  3. https://www.eclipse.org.

  4. https://www.jetbrains.com/.

Abbreviations

AD:

Action detection

AI:

Artificial intelligence

AP:

Average precision

AUC:

Area under the curve

CAM:

Class Activation Map

CL:

Classification

CV:

Computer vision

DNN:

Deep Neural Network

ET:

Error type

FN:

False negative

FP:

False positive

GT:

Ground truth

IoU:

Intersection over union

IS:

Instance segmentation

MAE:

Mean absolute error

mAP:

Mean average precision

ME:

Mean error

ML:

Machine learning

MSE:

Mean squared error

NAB:

Numenta anomaly benchmark

NLP:

Natural language processing

OD:

Object detection

OT:

Object tracking

PE:

Pose estimation

PR:

Precision–recall

RMSE:

Root mean squared error

ROC:

Receiver operating characteristic

RS:

Recommender systems

SS:

Semantic segmentation

TN:

True negative

TP:

True positive

TS:

Time series

VRD:

Video relation detection

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Acknowledgements

This work is partially supported by the project “PRECEPT - A novel decentralized edge-enabled PREsCriptivE and ProacTive framework for increased energy efficiency and well-being in residential buildings” funded by the EU H2020 Programme, Grant Agreement No. 958284.

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  1. Piero Fraternali, Federico Milani, Rocio Nahime Torres and Niccolò Zangrando contributed equally to this work.

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  1. Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Via Giuseppe Ponzio, 34, 20133, Milan, MI, Italy

    Piero Fraternali, Federico Milani, Rocio Nahime Torres & Niccolò Zangrando

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  1. Piero Fraternali
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Fraternali, P., Milani, F., Torres, R.N. et al. Black-box error diagnosis in Deep Neural Networks for computer vision: a survey of tools. Neural Comput & Applic 35, 3041–3062 (2023). https://doi.org/10.1007/s00521-022-08100-9

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