Summary of Investigating the Impact Of Hard Samples on Accuracy Reveals In-class Data Imbalance, by Pawel Pukowski and Haiping Lu
Investigating the Impact of Hard Samples on Accuracy Reveals In-class Data Imbalance
by Pawel Pukowski, Haiping Lu
First submitted to arxiv on: 22 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
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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 challenges the reliance on test accuracy as a primary metric for evaluating model efficacy in AutoML, highlighting issues with label noise and dataset imbalance. Researchers unveil two generalization pathways towards easy and hard samples, showing that the distribution of these samples between training and test sets affects perceived model performance. A proposed benchmarking procedure compares methods for identifying hard samples, promoting more nuanced approaches. The study aims to stimulate a critical discussion about model evaluation criteria, emphasizing the importance of considering in-class data imbalance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about how we evaluate models in machine learning. Right now, we mostly use test accuracy, which can be misleading because it doesn’t account for problems like noisy labels or datasets that are biased towards certain types of samples. The researchers found two ways that models generalize to these easy and hard samples, and they showed that the distribution of these samples affects how well a model performs. They also proposed a new way to compare different methods for identifying hard samples. Overall, this study wants to encourage more critical thinking about how we evaluate models and suggest new directions for research. |
Keywords
» Artificial intelligence » Generalization » Machine learning
