Robustness of Generalized Learning Vector Quantization Models against Adversarial Attacks

Adversarial attacks and the development of (deep) neural networks robust against them are currently two widely researched topics. The robustness of Learning Vector Quantization (LVQ) models against adversarial attacks has however not yet been studied to the same extent. We therefore present an extensive evaluation of three LVQ models: Generalized LVQ, Generalized Matrix LVQ and Generalized Tangent LVQ. The evaluation suggests that both Generalized LVQ and Generalized Tangent LVQ have a high base robustness, on par with the current state-of-the-art in robust neural network methods. In contrast to this, Generalized Matrix LVQ shows a high susceptibility to adversarial attacks, scoring consistently behind all other models. Additionally, our numerical evaluation indicates that increasing the number of prototypes per class improves the robustness of the models.Comment: to be published in 13th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualizatio

Provident vehicle detection at night for advanced driver assistance systems

In recent years, computer vision algorithms have become more powerful, which enabled technologies such as autonomous driving to evolve rapidly. However, current algorithms mainly share one limitation: They rely on directly visible objects. This is a significant drawback compared to human behavior, where visual cues caused by objects (e. g., shadows) are already used intuitively to retrieve information or anticipate occurring objects. While driving at night, this performance deficit becomes even more obvious: Humans already process the light artifacts caused by the headlamps of oncoming vehicles to estimate where they appear, whereas current object detection systems require that the oncoming vehicle is directly visible before it can be detected. Based on previous work on this subject, in this paper, we present a complete system that can detect light artifacts caused by the headlights of oncoming vehicles so that it detects that a vehicle is approaching providently (denoted as provident vehicle detection). For that, an entire algorithm architecture is investigated, including the detection in the image space, the three-dimensional localization, and the tracking of light artifacts. To demonstrate the usefulness of such an algorithm, the proposed algorithm is deployed in a test vehicle to use the detected light artifacts to control the glare-free high beam system proactively (react before the oncoming vehicle is directly visible). Using this experimental setting, the provident vehicle detection system’s time benefit compared to an in-production computer vision system is quantified. Additionally, the glare-free high beam use case provides a real-time and real-world visualization interface of the detection results by considering the adaptive headlamps as projectors. With this investigation of provident vehicle detection, we want to put awareness on the unconventional sensing task of detecting objects providently (detection based on observable visual cues the objects cause before they are visible) and further close the performance gap between human behavior and computer vision algorithms to bring autonomous and automated driving a step forward

The coming of age of interpretable and explainable machine learning models

Machine learning-based systems are now part of a wide array of real-world applications seamlessly embedded in the social realm. In the wake of this realisation, strict legal regulations for these systems are currently being developed, addressing some of the risks they may pose. This is the coming of age of the interpretability and explainability problems in machine learning-based data analysis, which can no longer be seen just as an academic research problem. In this tutorial, associated to ESANN 2021 special session on “Interpretable Models in Machine Learning and Explainable Artificial Intelligence”, we discuss explainable and interpretable machine learning as post-hoc and ante-hoc strategies to address these problems and highlight several aspects related to them, including their assessment. The contributions accepted for the session are then presented in this contextPeer ReviewedPostprint (published version

Robust Text Classification: Analyzing Prototype-Based Networks

Downstream applications often require text classification models to be accurate, robust, and interpretable. While the accuracy of the stateof-the-art language models approximates human performance, they are not designed to be interpretable and often exhibit a drop in performance on noisy data. The family of PrototypeBased Networks (PBNs) that classify examples based on their similarity to prototypical examples of a class (prototypes) is natively interpretable and shown to be robust to noise, which enabled its wide usage for computer vision tasks. In this paper, we study whether the robustness properties of PBNs transfer to text classification tasks. We design a modular and comprehensive framework for studying PBNs, which includes different backbone architectures, backbone sizes, and objective functions. Our evaluation protocol assesses the robustness of models against character-, word-, and sentence-level perturbations. Our experiments on three benchmarks show that the robustness of PBNs transfers to NLP classification tasks facing realistic perturbations. Moreover, the robustness of PBNs is supported mostly by the objective function that keeps prototypes interpretable, while the robustness superiority of PBNs over vanilla models becomes more salient as datasets get more complex

New Prototype Concepts in Classification Learning

Saralajew S. New Prototype Concepts in Classification Learning. Bielefeld: Universität Bielefeld; 2020.Machine learning algorithms are becoming more and more important in everyday life. Applications in search engines, driver assistance systems, consumer electronics, and so on use them heavily and would not be as powerful without them. Neural Networks (NNs), for example, are state-of-the-art classification approaches and dominate the field. However, they are difficult to interpret and not fully understood. For instance, the existence of adversarial examples that are imperceptible to humans contradicts the general belief that convolutional NNs classify objects in images mainly by breaking them down into increasingly complex object shapes. In this thesis, we study prototype-based classification algorithms with the goal of improving the classification capabilities of such algorithms while simultaneously preserving robustness and interpretability properties. Moreover, we investigate how properties of prototype-based classification algorithms can be transferred to NNs in order to increase their interpretability. First, we derive the concept of set-prototypes and apply it in a Learning Vector Quantization (LVQ) framework—a well-understood classification algorithm. We examine the mathematical properties and show that the derived method is provably robust against adversarial attacks. Furthermore, the method consistently outperforms other LVQ approaches while still being interpretable. Second, we relax the class-specific prototype concept to that of components and apply it in LVQ- and NN-based classifiers. This framework provides promising interpretation techniques for NNs. For example, we use them to explain how an adversarial attack is fooling an NN. We evaluate the methods on both toy and real-world datasets, including Indian Pine, MNIST, CIFAR-10, GTSRB, and ImageNet

Inverse Berechnung von Werkstoffkennwerten durch FEM und Evolutionsstrategien

Die vorliegende Arbeit befasst sichmit der Entwicklung eines Berechnungsverfahrens, um Werk-stoffkennwerte eines beliebigen Werkstoffes invers aus einer elastischen Verformung zu ermit-teln. Dazu wird eine Evolutionsstrategie hergeleitet, mit FEM Modellen gekoppelt und auf ideale und reale Beispiele angewendet. Um die Leistungsfähigkeit dieser Evolutionsstrategie aus ma-thematischer Sicht einzuordnen, wird sie an diversen mathematischen Testfunktionen getestet. Damit die Grenzen dieser Werkstoffkennwerteberechnung ersichtlich sind, wird das zu Grun-de liegende Optimierungsproblem hergeleitet, die Theorie der Evolutionsstrategien beschrieben und ausführliche Hinweise zur Modellierung der benötigten FEM Modelle gegeben. Für die An-wendung dieses Verfahrens zur Werkstoffkennwerteberechnung wird in der Entwicklungsumge-bung MATLAB® ein Programm entwickelt. Dieses Programm arbeitet zur FEM Berechnung mit ANSYS® und stellt eine Benutzeroberfläche zur praktischen Anwendung bereit

The Resolved Mutual Information Function as a Structural Fingerprint of Biomolecular Sequences for Interpretable Machine Learning Classifiers

In the present article we propose the application of variants of the mutual information function as characteristic fingerprints of biomolecular sequences for classification analysis. In particular, we consider the resolved mutual information functions based on Shannon-, Rényi-, and Tsallis-entropy. In combination with interpretable machine learning classifier models based on generalized learning vector quantization, a powerful methodology for sequence classification is achieved which allows substantial knowledge extraction in addition to the high classification ability due to the model-inherent robustness. Any potential (slightly) inferior performance of the used classifier is compensated by the additional knowledge provided by interpretable models. This knowledge may assist the user in the analysis and understanding of the used data and considered task. After theoretical justification of the concepts, we demonstrate the approach for various example data sets covering different areas in biomolecular sequence analysis