Summary of Denetdm: Debiasing by Network Depth Modulation, By Silpa Vadakkeeveetil Sreelatha et al.
DeNetDM: Debiasing by Network Depth Modulation
by Silpa Vadakkeeveetil Sreelatha, Adarsh Kappiyath, Abhra Chaudhuri, Anjan Dutta
First submitted to arxiv on: 28 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 A new paper proposes a method to debias neural networks trained on biased datasets, which often learn spurious correlations that hinder generalization. The authors formally prove two key insights: (1) samples with spurious correlations lie on a lower rank manifold than those without, and (2) the depth of a network acts as an implicit regularizer on the attribute subspace encoded in its representations. Leveraging these findings, they introduce DeNetDM, a debiasing method that modulates network depth to develop robustness against spurious correlations. The approach requires no bias annotations or explicit data augmentation and outperforms existing methods on synthetic and real-world datasets by 5%. The authors demonstrate the effectiveness of DeNetDM using a training paradigm derived from Product of Experts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DeNetDM is a new way to make neural networks less biased when they’re trained on bad data. This happens because the network starts to learn relationships that aren’t really there, which makes it hard for the network to work well in the real world. The researchers found two important things: (1) the “bad” samples are special and have something different about them, and (2) how deep a network is can help make it less biased. They used this knowledge to create DeNetDM, which makes networks more robust against these bad relationships. This method works just as well as other methods that need extra information or work, but it’s simpler. The researchers tested DeNetDM on fake and real data and found that it was 5% better than the old ways. |
Keywords
* Artificial intelligence * Data augmentation * Generalization
