arXiv subjects

Patrick Zschech

Publications and source records attributed to Patrick Zschech.

24 records · Page 2Linked to original sources

arXiv2022

GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints

The number of information systems (IS) studies dealing with explainable artificial intelligence (XAI) is currently exploding as the field demands more transparency about the internal decision logic of machine learning (ML) models. However, most techniques subsumed under XAI provide post-hoc-analytical explanations, which have to be considered with caution as they only use approximations of the underlying ML model. Therefore, our paper investigates a series of intrinsically interpretable ML models and discusses their suitability for the IS community. More specifically, our focus is on advanced extensions of generalized additive models (GAM) in which predictors are modeled independently in a non-linear way to generate shape functions that can capture arbitrary patterns but remain fully interpretable. In our study, we evaluate the prediction qualities of five GAMs as compared to six traditional ML models and assess their visual outputs for model interpretability. On this basis, we investigate their merits and limitations and derive design implications for further improvements.

cs.LG↗

arXiv2022

A Light in the Dark: Deep Learning Practices for Industrial Computer Vision

In recent years, large pre-trained deep neural networks (DNNs) have revolutionized the field of computer vision (CV). Although these DNNs have been shown to be very well suited for general image recognition tasks, application in industry is often precluded for three reasons: 1) large pre-trained DNNs are built on hundreds of millions of parameters, making deployment on many devices impossible, 2) the underlying dataset for pre-training consists of general objects, while industrial cases often consist of very specific objects, such as structures on solar wafers, 3) potentially biased pre-trained DNNs raise legal issues for companies. As a remedy, we study neural networks for CV that we train from scratch. For this purpose, we use a real-world case from a solar wafer manufacturer. We find that our neural networks achieve similar performances as pre-trained DNNs, even though they consist of far fewer parameters and do not rely on third-party datasets.

cs.CV↗

arXiv2021

A Picture is Worth a Collaboration: Accumulating Design Knowledge for Computer-Vision-based Hybrid Intelligence Systems

Computer vision (CV) techniques try to mimic human capabilities of visual perception to support labor-intensive and time-consuming tasks like the recognition and localization of critical objects. Nowadays, CV increasingly relies on artificial intelligence (AI) to automatically extract useful information from images that can be utilized for decision support and business process automation. However, the focus of extant research is often exclusively on technical aspects when designing AI-based CV systems while neglecting socio-technical facets, such as trust, control, and autonomy. For this purpose, we consider the design of such systems from a hybrid intelligence (HI) perspective and aim to derive prescriptive design knowledge for CV-based HI systems. We apply a reflective, practice-inspired design science approach and accumulate design knowledge from six comprehensive CV projects. As a result, we identify four design-related mechanisms (i.e., automation, signaling, modification, and collaboration) that inform our derived meta-requirements and design principles. This can serve as a basis for further socio-technical research on CV-based HI systems.

cs.CV↗

arXiv2021

A survey of image labelling for computer vision applications

Supervised machine learning methods for image analysis require large amounts of labelled training data to solve computer vision problems. The recent rise of deep learning algorithms for recognising image content has led to the emergence of many ad-hoc labelling tools. With this survey, we capture and systematise the commonalities as well as the distinctions between existing image labelling software. We perform a structured literature review to compile the underlying concepts and features of image labelling software such as annotation expressiveness and degree of automation. We structure the manual labelling task by its organisation of work, user interface design options, and user support techniques to derive a systematisation schema for this survey. Applying it to available software and the body of literature, enabled us to uncover several application archetypes and key domains such as image retrieval or instance identification in healthcare or television.

cs.CV↗

arXiv2021

Machine learning and deep learning

Today, intelligent systems that offer artificial intelligence capabilities often rely on machine learning. Machine learning describes the capacity of systems to learn from problem-specific training data to automate the process of analytical model building and solve associated tasks. Deep learning is a machine learning concept based on artificial neural networks. For many applications, deep learning models outperform shallow machine learning models and traditional data analysis approaches. In this article, we summarize the fundamentals of machine learning and deep learning to generate a broader understanding of the methodical underpinning of current intelligent systems. In particular, we provide a conceptual distinction between relevant terms and concepts, explain the process of automated analytical model building through machine learning and deep learning, and discuss the challenges that arise when implementing such intelligent systems in the field of electronic markets and networked business. These naturally go beyond technological aspects and highlight issues in human-machine interaction and artificial intelligence servitization.

cs.AI↗

arXiv2021

labelCloud: A Lightweight Domain-Independent Labeling Tool for 3D Object Detection in Point Clouds

Within the past decade, the rise of applications based on artificial intelligence (AI) in general and machine learning (ML) in specific has led to many significant contributions within different domains. The applications range from robotics over medical diagnoses up to autonomous driving. However, nearly all applications rely on trained data. In case this data consists of 3D images, it is of utmost importance that the labeling is as accurate as possible to ensure high-quality outcomes of the ML models. Labeling in the 3D space is mostly manual work performed by expert workers, where they draw 3D bounding boxes around target objects the ML model should later automatically identify, e.g., pedestrians for autonomous driving or cancer cells within radiography. While a small range of recent 3D labeling tools exist, they all share three major shortcomings: (i) they are specified for autonomous driving applications, (ii) they lack convenience and comfort functions, and (iii) they have high dependencies and little flexibility in data format. Therefore, we propose a novel labeling tool for 3D object detection in point clouds to address these shortcomings.

cs.CV↗