Towards Gradient-based Time-Series Explanations through a SpatioTemporal Attention Network
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911889656643584 |
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| author | Lee, Min Hun |
| author_facet | Lee, Min Hun |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_17444 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Gradient-based Time-Series Explanations through a SpatioTemporal Attention Network Lee, Min Hun Computer Vision and Pattern Recognition Machine Learning 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. |
| title | Towards Gradient-based Time-Series Explanations through a SpatioTemporal Attention Network |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2405.17444 |