Summary of Evaluating the Generalization Ability Of Spatiotemporal Model in Urban Scenario, by Hongjun Wang et al.
Evaluating the Generalization Ability of Spatiotemporal Model in Urban Scenario
by Hongjun Wang, Jiyuan Chen, Tong Pan, Zheng Dong, Lingyu Zhang, Renhe Jiang, Xuan Song
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Databases (cs.DB)
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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 Spatiotemporal neural networks have shown great promise in urban scenarios by effectively capturing temporal and spatial correlations. However, their generalization ability to out-of-distribution settings remains largely unexplored. To address this, researchers propose the Spatiotemporal Out-of-Distribution (ST-OOD) benchmark, comprising six urban scenarios with in-distribution and out-of-distribution settings. They extensively evaluate state-of-the-art spatiotemporal models and find that their performance degrades significantly in out-of-distribution settings, with most models performing worse than a simple Multi-Layer Perceptron (MLP). The study suggests that current leading methods over-rely on parameters to overfit training data, leading to poor generalization. Additionally, the researchers investigate whether dropout can mitigate this issue and find that a slight dropout rate can improve generalization performance while minimizing impact on in-distribution performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how well neural networks do in urban environments when they’re tested with new information they haven’t seen before. Currently, these models are only tested on data from the same year or a few weeks later. The researchers created a special benchmark to test these models and found that they don’t do very well when given new information. They think this is because the models are too focused on fitting the training data and not enough on learning general patterns. The study also looked at whether adding some randomness to the model could help it generalize better, and found that a little bit of randomness can make a big difference. |
Keywords
» Artificial intelligence » Dropout » Generalization » Spatiotemporal
