Summary of Debiasify: Self-distillation For Unsupervised Bias Mitigation, by Nourhan Bayasi et al.
Debiasify: Self-Distillation for Unsupervised Bias Mitigation
by Nourhan Bayasi, Jamil Fayyad, Ghassan Hamarneh, Rafeef Garbi, Homayoun Najjaran
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 novel self-distillation approach, Debiasify, tackles simplicity bias in neural networks by transferring knowledge from complex features to simpler attribute-conditioned features without prior knowledge of biases. This unsupervised method learns robust and debiased representations that generalize well across diverse biases and datasets, improving worst-group performance and overall accuracy. Extensive experiments on computer vision and medical imaging benchmarks demonstrate Debiasify’s effectiveness, outperforming previous unsupervised debiasing methods by up to 10.13% in worst-group accuracy for Wavy Hair classification in CelebA. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Debiasify is a new way to fix neural networks that are too simple. Sometimes, these networks learn things that aren’t really important just because they’re easy. This can make the network do badly when it’s shown new things. Debiasify helps by taking what the network has learned and moving it from deep parts of the network (where it’s complex) to simpler parts. This makes the network better at doing new things and being fair. |
Keywords
» Artificial intelligence » Classification » Distillation » Unsupervised
