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1. Memory-based Distribution Shift Detection for Learning Enabled Cyber-Physical Systems with Statistical Guarantees

   https://doi.org/10.1145/3643892  

   Yang, Yahan ; Kaur, Ramneet ; Dutta, Souradeep ; Lee, Insup ( April 2024 , ACM Transactions on Cyber-Physical Systems)

   Incorporating learning based components in the current state-of-the-art cyber-physical systems (CPS) has been a challenge due to the brittleness of the underlying deep neural networks. On the bright side, if executed correctly with safety guarantees, this has the ability to revolutionize domains like autonomous systems, medicine, and other safety-critical domains. This is because it would allow system designers to use high-dimensional outputs from sensors like camera and LiDAR. The trepidation in deploying systems with vision and LiDAR components comes from incidents of catastrophic failures in the real world. Recent reports of self-driving cars running into difficult to handle scenarios is ingrained in the software components which handle such sensor inputs.

   The ability to handle such high-dimensional signals is due to the explosion of algorithms which use deep neural networks. Sadly, the reason behind the safety issues is also due to deep neural networks themselves. The pitfalls occur due to possible over-fitting and lack of awareness about the blind spots induced by the training distribution. Ideally, system designers would wish to cover as many scenarios during training as possible. However, achieving a meaningful coverage is impossible. This naturally leads to the following question: is it feasible to flag out-of-distribution (OOD) samples without causing too many false alarms? Such an OOD detector should be executable in a fashion that is computationally efficient. This is because OOD detectors often are executed as frequently as the sensors are sampled.

   Our aim in this article is to build an effective anomaly detector. To this end, we propose the idea of a memory bank to cache data samples which are representative enough to cover most of the in-distribution data. The similarity with respect to such samples can be a measure of familiarity of the test input. This is made possible by an appropriate choice of distance function tailored to the type of sensor we are interested in. Additionally, we adapt conformal anomaly detection framework to capture the distribution shifts with a guarantee of false alarm rate. We report the performance of our technique on two challenging scenarios: a self-driving car setting implemented inside the simulator CARLA with image inputs and autonomous racing car navigation setting with LiDAR inputs. From the experiments, it is clear that a deviation from the in-distribution setting can potentially lead to unsafe behavior. It should be noted that not all OOD inputs lead to precarious situations in practice, but staying in-distribution is akin to staying within a safety bubble and predictable behavior. An added benefit of our memory-based approach is that the OOD detector produces interpretable feedback for a human designer. This is of utmost importance since it recommends a potential fix for the situation as well. In other competing approaches, such feedback is difficult to obtain due to reliance on techniques which use variational autoencoders. 

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2. Runtime Monitoring of Deep Neural Networks Using Top-Down Context Models Inspired by Predictive Processing and Dual Process Theory

   Roy, A. ; Cobb, A. ; Bastian, Nathaniel ; Jalaian, Brian ; Jha, Susmit ( April 2022 , AAAI Spring Symposium 2022)

   Deep neural networks (DNNs) have achieved near-human level accuracy on many datasets across different domains. But they are known to produce incorrect predictions with high confidence on inputs far from the training distribution. This challenge of lack of calibration of DNNs has limited the adoption of deep learning models in high-assurance systems such as autonomous driving, air traffic management, cybersecurity, and medical diagnosis. The problem of detecting when an input is outside the training distribution of a machine learning model, and hence, its prediction on this input cannot be trusted, has received significant attention recently. Several techniques based on statistical, geometric, topological, or relational signatures have been developed to detect the out-of-distribution (OOD) or novel inputs. In this paper, we present a runtime monitor based on predictive processing and dual process theory. We posit that the bottom-up deep neural networks can be monitored using top-down context models comprising two layers. The first layer is a feature density model that learns the joint distribution of the original DNN’s inputs, outputs, and the model’s explanation for its decisions. The second layer is a graph Markov neural network that captures an even broader context. We demonstrate the efficacy of our monitoring architecture in recognizing out-of-distribution and out-of-context inputs on the image classification and object detection tasks. 

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3. Task-Aware Novelty Detection for Visual-based Deep Learning in Autonomous Systems

   Chen, Valerie ; Yoon, Man-Ki ; Shao, Zhong ( May 2020 , 2020 IEEE International Conference on Robotics and Automation (ICRA))

   Deep-learning driven safety-critical autonomous systems, such as self-driving cars, must be able to detect situations where its trained model is not able to make a trustworthy prediction. This ability to determine the novelty of a new input with respect to a trained model is critical for such systems because novel inputs due to changes in the environment, adversarial attacks, or even unintentional noise can potentially lead to erroneous, perhaps life-threatening decisions. This paper proposes a learning framework that leverages information learned by the prediction model in a task-aware manner to detect novel scenarios. We use network saliency to provide the learning architecture with knowledge of the input areas that are most relevant to the decision-making and learn an association between the saliency map and the predicted output to determine the novelty of the input. We demonstrate the efficacy of this method through experiments on real-world driving datasets as well as through driving scenarios in our in-house indoor driving environment where the novel image can be sampled from another similar driving dataset with similar features or from adversarial attacked images from the training dataset. We find that our method is able to systematically detect novel inputs and quantify the deviation from the target prediction through this task-aware approach. 

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4. End-to-end Learning of a Convolutional Neural Network via Deep Tensor Decomposition.

   Oymak, Samet ; Soltanolkotabi, Mahdi ( January 2021 , Information and inference)

   In this paper we study the problem of learning the weights of a deep convolutional neural network. We consider a network where convolutions are carried out over non-overlapping patches with a single kernel in each layer. We develop an algorithm for simultaneously learning all the kernels from the training data. Our approach dubbed Deep Tensor Decomposition (DeepTD1 ) is based on a rank-1 tensor decomposition. We theoretically investigate DeepTD under a realizable model for the training data where the inputs are chosen i.i.d. from a Gaussian distribution and the labels are generated according to planted convolutional kernels. We show that DeepTD is data-efficient and provably works as soon as the sample size exceeds the total number of convolutional weights in the network. We carry out a variety of numerical experiments to investigate the effectiveness of DeepTD and verify our theoretical findings. 

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5. Input Distribution Coverage: Measuring Feature Interaction Adequacy in Neural Network Testing

   https://doi.org/10.1145/3576040  

   Dola, Swaroopa ; Dwyer, Matthew B. ; Soffa, Mary Lou ( July 2023 , ACM Transactions on Software Engineering and Methodology)

   Testing deep neural networks (DNNs) has garnered great interest in the recent years due to their use in many applications. Black-box test adequacy measures are useful for guiding the testing process in covering the input domain. However, the absence of input specifications makes it challenging to apply black-box test adequacy measures in DNN testing. The Input Distribution Coverage (IDC) framework addresses this challenge by using a variational autoencoder to learn a low dimensional latent representation of the input distribution, and then using that latent space as a coverage domain for testing. IDC applies combinatorial interaction testing on a partitioning of the latent space to measure test adequacy. Empirical evaluation demonstrates that IDC is cost-effective, capable of detecting feature diversity in test inputs, and more sensitive than prior work to test inputs generated using different DNN test generation methods. The findings demonstrate that IDC overcomes several limitations of white-box DNN coverage approaches by discounting coverage from unrealistic inputs and enabling the calculation of test adequacy metrics that capture the feature diversity present in the input space of DNNs. 

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