DETOXER: A Visual Debugging Tool With Multiscope Explanations for Temporal Multilabel Classification

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.

Automated summary

This article proposes DETOXER, an interactive visual debugging system, to support finding different error types and scopes in video activity recognition.