Summary of Where Is the Truth? the Risk Of Getting Confounded in a Continual World, by Florian Peter Busch et al.
Where is the Truth? The Risk of Getting Confounded in a Continual World
by Florian Peter Busch, Roshni Kamath, Rupert Mitchell, Wolfgang Stammer, Kristian Kersting, Martin Mundt
First submitted to arxiv on: 9 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 paper addresses the challenge of confounded datasets in continual learning scenarios, where spurious correlations can easily arise and fail to generalize to new data. The authors demonstrate that the problem of mitigating confounders is more significant than the standard forgetting problem, even when training on all tasks jointly. A novel dataset, ConCon, is constructed using CLEVR-based confounded data, which reveals that standard continual learning methods are ineffective in ignoring these confounders. The work highlights the importance of developing robust continual learning methods to tackle confounding factors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a nutshell, this research paper explores how datasets can be “confounded” by spurious correlations that don’t apply to new situations. This is especially tricky when trying to learn continuously as our understanding and tasks evolve over time. The authors introduce a special dataset called ConCon that shows us just how hard it is for traditional learning methods to ignore these confounding factors. They want us to develop better ways to handle this challenge. |
Keywords
* Artificial intelligence * Continual learning
