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  • 1.
    Luan, Siyu
    et al.
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Gu, Zonghua
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Saremi, Amin
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Freidovich, Leonid B.
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Jiang, Lili
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Wan, Shaohua
    Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, China.
    Timing performance benchmarking of out-of-distribution detection algorithms2023In: Journal of Signal Processing Systems, ISSN 1939-8018, E-ISSN 1939-8115, Vol. 95, no 12, p. 1355-1370Article in journal (Refereed)
    Abstract [en]

    In an open world with a long-tail distribution of input samples, Deep Neural Networks (DNNs) may make unpredictable mistakes for Out-of-Distribution (OOD) inputs at test time, despite high levels of accuracy obtained during model training. OOD detection can be an effective runtime assurance mechanism for safe deployment of machine learning algorithms in safety–critical applications such as medical imaging and autonomous driving. A large number of OOD detection algorithms have been proposed in recent years, with a wide range of performance metrics in terms of accuracy and execution time. For real-time safety–critical applications, e.g., autonomous driving, timing performance is of great importance in addition to accuracy. We perform a comprehensive and systematic benchmark study of multiple OOD detection algorithms in terms of both accuracy and execution time on different hardware platforms, including a powerful workstation and a resource-constrained embedded device, equipped with both CPU and GPU. We also profile and analyze the internal details of each algorithm to identify the performance bottlenecks and potential for GPU acceleration. This paper aims to provide a useful reference for the practical deployment of OOD detection algorithms for real-time safety–critical applications.

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  • 2.
    Luan, Siyu
    et al.
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Gu, Zonghua
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Wan, Shaohua
    Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, China.
    Efficient performance prediction of end-to-end autonomous driving under continuous distribution shifts based on anomaly detection2023In: Journal of Signal Processing Systems, ISSN 1939-8018, E-ISSN 1939-8115, Vol. 95, no 12, p. 1455-1468Article in journal (Refereed)
    Abstract [en]

    A Deep Neural Network (DNN)’s prediction may be unreliable outside of its training distribution despite high levels of accuracy obtained during model training. The DNN may experience different degrees of accuracy degradation for different levels of distribution shifts, hence it is important to predict its performance (accuracy) under distribution shifts. In this paper, we consider the end-to-end approach to autonomous driving of using a DNN to map from an input image to the control action such as the steering angle. For each input image with possible perturbations that cause distribution shifts, we design a Performance Prediction Module to compute its anomaly score, and use it to predict the DNN’s expected prediction error, i.e., its expected deviation from the ground truth (optimal) control action, which is not available after deployment. If the expected prediction error is too large, then the DNN’s prediction may no longer be trusted, and remedial actions should be taken to ensure safety. We consider different methods for computing the anomaly score for the input image, including using the reconstruction error of an Autoencoder, or applying an Anomaly Detection algorithm to a hidden layer of the DNN. We present performance evaluation of the different methods in terms of both prediction accuracy and execution time on different hardware platforms, in order to provide a useful reference for the designer to choose among the different methods.

    Download full text (pdf)
    fulltext
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