Towards Gradient-based Time-Series Explanations through a SpatioTemporal Attention Network

Min Hun Lee

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arXiv · 2405.17444

Towards Gradient-based Time-Series Explanations through a SpatioTemporal Attention Network

Min Hun Lee

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Abstract

In this paper, we explore the feasibility of using a transformer-based, spatiotemporal attention network (STAN) for gradient-based time-series explanations. First, we trained the STAN model for video classifications using the global and local views of data and weakly supervised labels on time-series data (i.e. the type of an activity). We then leveraged a gradient-based XAI technique (e.g. saliency map) to identify salient frames of time-series data. According to the experiments using the datasets of four medically relevant activities, the STAN model demonstrated its potential to identify important frames of videos.

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Min Hun Lee. 2024-05-18. Towards Gradient-based Time-Series Explanations through a SpatioTemporal Attention Network. https://arxiv.org/abs/2405.17444

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