Summary of Unveiling and Mitigating Generalized Biases Of Dnns Through the Intrinsic Dimensions Of Perceptual Manifolds, by Yanbiao Ma et al.
Unveiling and Mitigating Generalized Biases of DNNs through the Intrinsic Dimensions of Perceptual Manifolds
by Yanbiao Ma, Licheng Jiao, Fang Liu, Lingling Li, Wenping Ma, Shuyuan Yang, Xu Liu, Puhua Chen
First submitted to arxiv on: 22 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper proposes a geometric perspective for analyzing the fairness of deep neural networks (DNNs) by examining how DNNs shape the intrinsic geometric characteristics of datasets, specifically the intrinsic dimensions (IDs) of perceptual manifolds. The authors identify limitations in current methods for predicting DNN biases and introduce Intrinsic Dimension Regularization (IDR), a technique that enhances fairness and performance by promoting concise and ID-balanced class perceptual manifolds. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making sure artificial intelligence systems are fair and don’t have biases. Right now, there’s no good way to measure these biases, so the authors created a new approach called Intrinsic Dimension Regularization (IDR). IDR helps make models more fair and accurate by learning from data in a way that balances different classes of information. |
Keywords
» Artificial intelligence » Regularization
