Summary of Towards Diverse Perspective Learning with Selection Over Multiple Temporal Poolings, by Jihyeon Seong et al.
Towards Diverse Perspective Learning with Selection over Multiple Temporal Poolings
by Jihyeon Seong, Jungmin Kim, Jaesik Choi
First submitted to arxiv on: 14 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed novel temporal pooling method, Selection over Multiple Temporal Poolings (SoM-TP), dynamically selects the optimal temporal pooling among multiple methods for each data by attention. This approach is motivated by the ensemble concept of Multiple Choice Learning (MCL) and enables a non-iterative pooling ensemble within a single classifier. The SoM-TP method also defines a perspective loss and Diverse Perspective Learning Network (DPLN), which regularizes all pooling perspectives from the DPLN. A perspective analysis using Layer-wise Relevance Propagation (LRP) reveals the limitation of a single perspective and demonstrates the diverse perspective learning capabilities of SoM-TP. The proposed method outperforms CNN models based on other temporal poolings and state-of-the-art models in Time Series Classification (TSC), as demonstrated by extensive experiments on UCR/UEA repositories. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to analyze time series data, called Selection over Multiple Temporal Poolings (SoM-TP). This method looks at different ways of processing sequential information and chooses the best one for each piece of data. It’s like a team working together, where each person has their own strengths and weaknesses, but they can work together to get better results. The paper shows that this approach works better than others in analyzing time series data. |
Keywords
* Artificial intelligence * Attention * Classification * Cnn * Time series
