Towards Gradient-based Time-Series Explanations through a SpatioTemporal Attention Network
<|reference_start|>Towards Gradient-based Time-Series Explanations through a SpatioTemporal Attention Network: 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.<|reference_end|>
@article{lee2024towards,
    title={Towards Gradient-based Time-Series Explanations through a SpatioTemporal
  Attention Network},
    author={Min Hun Lee},
    journal={arXiv preprint arXiv:2405.17444},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.17444},
    primaryClass={cs.CV cs.LG}
}