<ul data-eligibleForWebStory="false"><li>Saliency methods, used for visual validation in image and language processing, face challenges when applied to time series data due to its complexity and diverse nature.</li><li>A study investigated various saliency methods on time series data to provide recommendations for interpreting convolutional models, particularly focusing on the tool-use time series dataset.</li><li>The study used nine different post-hoc saliency methods on six real-world datasets, evaluating them with five metrics to offer guidance on choosing suitable methods for interpreting models.</li><li>Results showed that no single saliency method consistently outperformed others across all metrics, but the study provides insights and guidelines to help experts select appropriate methods for specific models and datasets.</li></ul>

Don't Get Me Wrong: How to Apply Deep Visual Interpretations to Time Series

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