Industrial anomaly detection with normalizing flows

This thesis addresses deep learning-based methods for automatic anomaly detection in an industrial context. It involves image- or sensor-based detection of defects in the production process that can affect the quality of products. Automating this task provides a reliable and cost-effective alternative to humans, who perform this task manually by sighting. Since this setup has special requirements such as detecting previously unknown defects that traditional approaches cannot fulfill, this paper presents anomaly detection methods that learn without any examples of anomalies and include only normal data in the training process. Most of our proposed methods address the problem from a statistical perspective. Based on a deep-learning-based density estimation of the normal data, it is assumed that anomalies are considered unlikely according to the modeled distribution. The density estimation is performed by so-called \textit{Normalizing Flows}, which, in contrast to conventional neural networks, can model a formally valid probability distribution due to their bijective mapping. Moreover, due to their flexibility, Normalizing Flows allow modeling of more complex distributions in contrast to traditional methods, which usually use strong simplifications about the distribution

Q-SENN: Quantized Self-Explaining Neural Networks

Explanations in Computer Vision are often desired, but most Deep Neural Networks can only provide saliency maps with questionable faithfulness. Self-Explaining Neural Networks (SENN) extract interpretable concepts with fidelity, diversity, and grounding to combine them linearly for decision-making. While they can explain what was recognized, initial realizations lack accuracy and general applicability. We propose the Quantized-Self-Explaining Neural Network Q-SENN. Q-SENN satisfies or exceeds the desiderata of SENN while being applicable to more complex datasets and maintaining most or all of the accuracy of an uninterpretable baseline model, out-performing previous work in all considered metrics. Q-SENN describes the relationship between every class and feature as either positive, negative or neutral instead of an arbitrary number of possible relations, enforcing more binary human-friendly features. Since every class is assigned just 5 interpretable features on average, Q-SENN shows convincing local and global interpretability. Additionally, we propose a feature alignment method, capable of aligning learned features with human language-based concepts without additional supervision. Thus, what is learned can be more easily verbalized. The code is published: https://github.com/ThomasNorr/Q-SENNComment: Accepted to AAAI 2024, SRRA

Mutation Control and Convergence in Evolutionary Multi-Object Optimization

This paper addresses the problem of controlling mutation strength in multi-objective evolutionary algorithms and its implications for the convergence to the Pareto set. Adaptive parameter control is one major issue in the field of evolutionary computation, and several methods have been proposed and applied successfully for single objective optimization problems. In this study we examine whether these results carry over to the multi-objective case and what modifications must be taken to meet the difficulties and pitfalls of conflicting objectives

Approximating the Pareto set

This paper adresses the problem of diversity in multiobjective evolutionary algorithms and its implications for the quality of the approximated set of efficient solutions (Pareto set). Current approaches for maintaining diversity are classified and related to the overall fitness assignment strategy. The resulting groups of complex selection operators are presented and tested on different objective functions exhibiting different levels of difficulty. For the assessment of the algorithmic performance a quality measure based on the notion of dominance is applied that reflects gain of information produced by the algorithm. This allows an online and time-dependent evaluation in order to characterize the dynamic behavior of an algorithm.This paper adresses the problem of diversity in multiobjective evolutionary algorithms and its implications for the quality of the approximated set of efficient solutions (Pareto set). Current approaches for maintaining diversity are classified and related to the overall fitness assignment strategy. The resulting groups of complex selection operators are presented and tested on different objective functions exhibiting different levels of difficulty. For the assessment of the algorithmic performance a quality measure based on the notion of dominance is applied that reflects gain of information produced by the algorithm. This allows an online and time-dependent evaluation in order to characterize the dynamic behavior of an algorithm

SplatPose & Detect: Pose-Agnostic 3D Anomaly Detection

Detecting anomalies in images has become a well-explored problem in both academia and industry. State-of-the-art algorithms are able to detect defects in increasingly difficult settings and data modalities. However, most current methods are not suited to address 3D objects captured from differing poses. While solutions using Neural Radiance Fields (NeRFs) have been proposed, they suffer from excessive computation requirements, which hinder real-world usability. For this reason, we propose the novel 3D Gaussian splatting-based framework SplatPose which, given multi-view images of a 3D object, accurately estimates the pose of unseen views in a differentiable manner, and detects anomalies in them. We achieve state-of-the-art results in both training and inference speed, and detection performance, even when using less training data than competing methods. We thoroughly evaluate our framework using the recently proposed Pose-agnostic Anomaly Detection benchmark and its multi-pose anomaly detection (MAD) data set.Comment: Visual Anomaly and Novelty Detection 2.0 Workshop at CVPR 202

The mature part of proNGF induces the structure of its pro-peptide

AbstractHuman nerve growth factor (NGF) belongs to the structural family of cystine knot proteins, characterized by a disulfide pattern in which one disulfide bond threads through a ring formed by a pair of two other disulfides connecting two adjacent β-strands. Oxidative folding of NGF revealed that the pro-peptide of NGF stimulates in vitro structure formation. In order to learn more about this folding assisting protein fragment, a biophysical analysis of the pro-peptide structure has been performed. While proNGF is a non-covalent homodimer, the isolated pro-peptide is monomeric. No tertiary contacts stabilize the pro-peptide in its isolated form. In contrast, the pro-peptide appears to be structured when bound to the mature part. The results presented here demonstrate that the mature part stabilizes the structure in the pro-peptide region. This is the first report that provides a biophysical analysis of a pro-peptide of the cystine knot protein family

CALCIUM RESPONSES IN FIBROBLASTS FROM ASYMPTOMATIC MEMBERS OF ALZHEIMER'S DISEASE FAMILIES

Abstract We have previously identified alterations of K + channel function, IP 3 -mediated calcium release, and Cp20 (a memory-associated GTP binding protein) in fibroblasts from Alzheimer's disease (AD) patients vs controls. Some of these alterations can be integrated into an index that distinguishes AD patients from controls with both high specificity and high sensitivity. We report here that alterations in IP 3 -mediated calcium responses are present in a large proportion of AD family members (i.e., individuals at high risk) before clinical symptoms of Alzheimer's disease are present. This was not the case if such members later "escaped" AD symptoms. This preclinical calcium signal correlate of later AD does not reflect, however, the presence of the PS1 familial AD gene

Virtual Reality via Object Pose Estimation and Active Learning:Realizing Telepresence Robots with Aerial Manipulation Capabilities

This paper presents a novel telepresence system for advancing aerial manipulation indynamic and unstructured environments. The proposed system not only features a haptic device, but also a virtual reality (VR) interface that provides real-time 3D displays of the robot’s workspace as well as a haptic guidance to its remotely located operator. To realize this, multiple sensors, namely, a LiDAR, cameras, and IMUs are utilized. For processing of the acquired sensory data, pose estimation pipelines are devised for industrial objects of both known and unknown geometries. We further propose an active learning pipeline in order to increase the sample efficiency of a pipeline component that relies on a Deep Neural Network (DNN) based object detector. All these algorithms jointly address various challenges encountered during the execution of perception tasks in industrial scenarios. In the experiments, exhaustive ablation studies are provided to validate the proposed pipelines. Method-ologically, these results commonly suggest how an awareness of the algorithms’ own failures and uncertainty (“introspection”) can be used to tackle the encountered problems. Moreover, outdoor experiments are conducted to evaluate the effectiveness of the overall system in enhancing aerial manipulation capabilities. In particular, with flight campaigns over days and nights, from spring to winter, and with different users and locations, we demonstrate over 70 robust executions of pick-and-place, force application and peg-in-hole tasks with the DLR cable-Suspended Aerial Manipulator (SAM). As a result, we show the viability of the proposed system in future industrial applications