Summary of Feed: Fairness-enhanced Meta-learning For Domain Generalization, by Kai Jiang et al.
FEED: Fairness-Enhanced Meta-Learning for Domain Generalization
by Kai Jiang, Chen Zhao, Haoliang Wang, Feng Chen
First submitted to arxiv on: 2 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 introduces an approach to fairness-aware meta-learning for domain generalization, aiming to find a set of fairness-aware invariant parameters that can achieve good generalization performance on unknown test domains while adhering to fairness constraints. The framework, Fairness-Enhanced Meta-Learning for Domain Generalization (FEED), disentangles latent data representations into content, style, and sensitive vectors, facilitating robust generalization across diverse domains. Unlike traditional methods focusing on domain invariance or sensitivity to shifts, FEED integrates a fairness-aware invariance criterion directly into the meta-learning process. This ensures that the learned parameters uphold fairness consistently even when domain characteristics vary widely. The approach is validated through extensive experiments across multiple benchmarks, demonstrating superior performance in maintaining high accuracy and fairness while improving significantly over existing state-of-the-art methods in domain generalization tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tackles a big problem in machine learning: how to make sure AI models are fair and work well even when the data they’re trained on is different from what they’ll be used with. The goal is to find a set of “fair” parameters that can be used across many different types of data, while making sure the model doesn’t discriminate against certain groups. The approach, called Fairness-Enhanced Meta-Learning for Domain Generalization (FEED), works by breaking down the data into three parts: what’s important (content), how it looks (style), and sensitive information (sensitive vectors). This helps the model learn to generalize well across different types of data, while also being fair. The approach is tested on many different datasets and shows that it can perform better than other state-of-the-art methods. |
Keywords
» Artificial intelligence » Domain generalization » Generalization » Machine learning » Meta learning
