PREVIS -- A Combined Machine Learning and Visual Interpolation Approach for Interactive Reverse Engineering in Assembly Quality Control
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Abstract (arXiv)
We present PREVIS, a visual analytics tool, enhancing machine learning performance analysis in engineering applications. The presented toolchain allows for a direct comparison of regression models. In addition, we provide a methodology to visualize the impact of regression errors on the underlying field of interest in the original domain, the part geometry, via exploiting standard interpolation methods. Further, we allow a real-time preview of user-driven parameter changes in the displacement field via visual interpolation. This allows for fast and accountable online change management. We demonstrate the effectiveness with an ex-ante optimization of an automotive engine hood.
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10 of 18 references.
References · 10
- 2013Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Mapscited by 2,348
- 2013Visualizing and Understanding Convolutional Networkscited by 2,278
- 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
- 2015Evaluating the visualization of what a Deep Neural Network has learnedcited by 416
- 2017Visualizing Deep Neural Network Decisions: Prediction Difference Analysiscited by 166
- 2017SGD Learns Over-parameterized Networks that Provably Generalize on Linearly Separable Datacited by 79
- 2019Can You Trust This Prediction? Auditing Pointwise Reliability After Learningcited by 28
- 2017Direct-Manipulation Visualization of Deep Networkscited by 26
- 2020StackGenVis: Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performance Metricscited by 13
- 2018Coloring black boxes: visualization of neural network decisionscited by 1
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