Summary of Deep Generative Domain Adaptation with Temporal Relation Knowledge For Cross-user Activity Recognition, by Xiaozhou Ye et al.
Deep Generative Domain Adaptation with Temporal Relation Knowledge for Cross-User Activity Recognition
by Xiaozhou Ye, Kevin I-Kai Wang
First submitted to arxiv on: 12 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
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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 paper introduces a Conditional Variational Autoencoder with Universal Sequence Mapping (CVAE-USM) approach to address the limitations of conventional domain adaptation methods in human activity recognition (HAR). The traditional assumption of independent and identically distributed (i.i.d.) data often fails, particularly in cross-user scenarios. CVAE-USM relaxes this assumption by leveraging temporal relations to align data distributions effectively across different users. This method combines VAE and USM to capture common patterns between users for improved activity recognition. The results, evaluated on OPPT and PAMAP2 datasets, demonstrate that CVAE-USM outperforms existing state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to recognize human activities when the data comes from different people. Right now, this task is tricky because the data is not always the same, even if it’s supposed to be. The researchers created a special kind of machine learning model that can deal with these differences and make better predictions. They tested their method on two big datasets and showed that it works much better than other methods. |
Keywords
* Artificial intelligence * Activity recognition * Domain adaptation * Machine learning * Variational autoencoder
