Summary of Cosfairnet:a Parameter-space Based Approach For Bias Free Learning, by Rajeev Ranjan Dwivedi et al.
CosFairNet:A Parameter-Space based Approach for Bias Free Learning
by Rajeev Ranjan Dwivedi, Priyadarshini Kumari, Vinod K Kurmi
First submitted to arxiv on: 19 Oct 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 Deep neural networks trained on biased data can inadvertently learn unintended inference rules, especially when labels are strongly correlated with biased features. Existing bias mitigation methods often address bias indirectly in the feature or sample space without controlling learned weights. Our novel approach addresses bias directly in the model’s parameter space, preventing its propagation across layers. We introduce two models: a bias model for biased features and a debias model for unbiased details, guided by the bias model. By enforcing dissimilarity in the debias model’s later layers and similarity in its initial layers with the bias model, our approach learns unbiased low-level features without adopting biased high-level abstractions. Our method shows enhanced classification accuracy and debiasing effectiveness across various synthetic and real-world datasets of different sizes, robustly addressing different bias types and percentages of biased samples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine if machines learned things from data that wasn’t fair or accurate. That’s what can happen when neural networks are trained on biased information. Existing solutions try to fix this problem by changing the way data is used during training, but they don’t directly control how the machine learns. Our new approach tries to solve this problem by teaching two models: one for biased information and one for fair details. We make sure these models learn different things as they go along, so that the model for fair details doesn’t pick up on the biased information. By doing this, we can improve how well machines classify data and reduce bias. Our method works well with different types of biased data and even handles situations where a small portion of the data is biased. |
Keywords
» Artificial intelligence » Classification » Inference
