EuroVA2022

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https://diglib.eg.org/handle/10.2312/2633197

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Browsing EuroVA2022 by Subject "Computing methodologies"

Now showing 1 - 2 of 2
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    A Comprehensive Workflow for Effective Imitation and Reinforcement Learning with Visual Analytics
    (The Eurographics Association, 2022) Metz, Yannick; Schlegel, Udo; Seebacher, Daniel; El-Assady, Mennatallah; Keim, Daniel; Bernard, Jürgen; Angelini, Marco
    Multiple challenges hinder the application of reinforcement learning algorithms in experimental and real-world use cases even with recent successes in such areas. Such challenges occur at different stages of the development and deployment of such models. While reinforcement learning workflows share similarities with machine learning approaches, we argue that distinct challenges can be tackled and overcome using visual analytic concepts. Thus, we propose a comprehensive workflow for reinforcement learning and present an implementation of our workflow incorporating visual analytic concepts integrating tailored views and visualizations for different stages and tasks of the workflow.
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    ScrutinAI: A Visual Analytics Approach for the Semantic Analysis of Deep Neural Network Predictions
    (The Eurographics Association, 2022) Haedecke, Elena; Mock, Michael; Akila, Maram; Bernard, Jürgen; Angelini, Marco
    We present ScrutinAI, a Visual Analytics approach to exploit semantic understanding for deep neural network (DNN) predictions analysis, focusing on models for object detection and semantic segmentation. Typical fields of application for such models, e.g. autonomous driving or healthcare, have a high demand for detecting and mitigating data- and model-inherent shortcomings. Our approach aims to help analysts use their semantic understanding to identify and investigate potential weaknesses in DNN models. ScrutinAI therefore includes interactive visualizations of the model's inputs and outputs, interactive plots with linked brushing, and data filtering with textual queries on descriptive meta data. The tool fosters hypothesis driven knowledge generation which aids in understanding the model's inner reasoning. Insights gained during the analysis process mitigate the ''black-box character'' of the DNN and thus support model improvement and generation of a safety argumentation for AI applications. We present a case study on the investigation of DNN models for pedestrian detection from the automotive domain.