Summary of Explainable Metric Learning For Deflating Data Bias, by Emma Andrews and Prabhat Mishra
Explainable Metric Learning for Deflating Data Bias
by Emma Andrews, Prabhat Mishra
First submitted to arxiv on: 5 Jul 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 paper presents an explainable metric learning framework for image classification, aiming to provide better interpretability of classification results. The framework constructs hierarchical levels of semantic segments in images using a bottom-up learning strategy. This approach enables a human-understandable similarity measurement between two images based on their semantic segments, which can be used to reduce bias in training datasets. Experimental evaluation shows that the proposed method improves model accuracy compared to state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making computers better at understanding pictures. It’s like teaching them to describe what they see. Right now, computers are good at recognizing things, but it’s hard for humans to understand why they made those decisions. The new approach helps make computer vision more human-friendly by breaking down images into smaller parts and measuring how similar or different they are. This can even help reduce mistakes in training data. |
Keywords
» Artificial intelligence » Classification » Image classification
