Summary of Complexity Matters: Dynamics Of Feature Learning in the Presence Of Spurious Correlations, by Guanwen Qiu et al.
Complexity Matters: Dynamics of Feature Learning in the Presence of Spurious Correlations
by GuanWen Qiu, Da Kuang, Surbhi Goel
First submitted to arxiv on: 5 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 research framework and synthetic dataset aim to study the dynamics of feature learning under spurious correlations in neural networks. The authors explore how the relative simplicity of spurious features affects their learning, alongside core features. The findings reveal several interesting phenomena, including slower learning rates for core features with stronger spurious correlations or simpler spurious features, the formation of distinct subnetworks for core and spurious feature learning, and the persistence of spurious features even after core features are fully learned. These results justify the success of retraining the last layer to remove spurious correlation and identify limitations of popular debiasing algorithms that exploit early learning of spurious features. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a special way to test how neural networks learn features when some features are easy to understand, but not important for the task. They found out that when these easy-to-understand features are strongly connected to the answer, it slows down how quickly the network can learn the really important features. The research also showed that two different groups of neurons form in the network: one group learns the easy features and another group learns the hard features. This means that even after the network has learned the important features, it still keeps track of the easy features. |
