DETOXER: A Visual Debugging Tool With Multiscope Explanations for Temporal Multilabel Classification
DETOXER: A Visual Debugging Tool With Multiscope Explanations for Temporal Multilabel Classification
复制标题
DETOXER：一种可视化调试工具，具有时态多标签分类的多范围解释
DOI：
10.1109/mcg.2022.3201465
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发表时间：
2022
影响因子：
1.8
通讯作者：
Gogate, Vibhav
中科院分区：
文献类型：
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作者：
Nourani, Mahsan;Roy, Chiradeep;Honeycutt, Donald R.;Ragan, Eric D.;Gogate, Vibhav
在许多应用中，开发的深度学习模型需要迭代调试和优化，以提高模型效率。一些模型，如时间多标签分类（TMLC），其中每个数据点可以同时属于多个类别，由于分析的复杂性和需要审查的实例，可能特别具有挑战性。在这篇文章中，视频活动识别作为TMLC的一个应用程序，我们提出DETOXER，一个交互式的可视化调试系统，通过提供多范围的解释，以支持发现不同的错误类型和范围。
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 proposeDETOXER, an interactive visual debugging system to support finding different error types and scopes through providing multiscope explanations.