DETOXER: A Visual Debugging Tool With Multiscope Explanations for Temporal Multilabel Classification
Mahsan Nourani -
Chiradeep Roy -
Donald R. Honeycutt -
Eric D. Ragan -
Vibhav Gogate -
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DOI: 10.1109/MCG.2022.3201465
Room: Bayshore III
2024-10-16T16:24:00ZGMT-0600Change your timezone on the schedule page
2024-10-16T16:24:00Z
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Keywords
Debugging, Analytical Models, Heating Systems, Data Models, Computational Modeling, Activity Recognition, Deep Learning, Multi Label Classification, Visualization Tool, Temporal Classification, Visual Debugging, False Positive, False Negative, Active Components, Deep Learning Models, Types Of Errors, Video Frames, Error Detection, Detection Of Types, Action Recognition, Interactive Visualization, Sequence Of Points, Design Goals, Positive Errors, Critical Outcomes, Error Patterns, Global Panel, False Negative Rate, False Positive Rate, Heatmap, Visual Approach, Truth Labels, True Positive, Confidence Score, Anomaly Detection, Interface Elements
Abstract
In many applications, developed deep-learning models need to be iteratively debugged and refined to improve the model efficiency over time. Debugging some models, such as temporal multilabel classification (TMLC) where each data point can simultaneously belong to multiple classes, can be especially more challenging due to the complexity of the analysis and instances that need to be reviewed. In this article, focusing on video activity recognition as an application of TMLC, we propose DETOXER, an interactive visual debugging system to support finding different error types and scopes through providing multiscope explanations.