Summary of Visual Data Diagnosis and Debiasing with Concept Graphs, by Rwiddhi Chakraborty et al.
Visual Data Diagnosis and Debiasing with Concept Graphs
by Rwiddhi Chakraborty, Yinong Wang, Jialu Gao, Runkai Zheng, Cheng Zhang, Fernando De la Torre
First submitted to arxiv on: 26 Sep 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 This paper presents ConBias, a novel framework for diagnosing and mitigating Concept co-occurrence Biases in visual datasets. The framework uses knowledge graphs of concepts to analyze spurious concept co-occurrences and uncover imbalances across the dataset. By employing a clique-based concept balancing strategy, ConBias can mitigate these imbalances, leading to improved performance on downstream tasks. Experimental results show that data augmentation based on a balanced concept distribution augmented by ConBias improves generalization performance compared to state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure machine learning models are fair and don’t make mistakes because of biases in the training data. It presents a new way to find and fix these biases, called ConBias. ConBias looks at visual datasets like pictures or videos as networks of concepts, like objects or actions. By analyzing these networks, it can identify problems where certain concepts are more likely to appear together than they should be. The paper shows that by fixing these biases, models become better at making predictions and avoiding mistakes. |
Keywords
» Artificial intelligence » Data augmentation » Generalization » Machine learning
