Summary of Multi-label Adaptive Batch Selection by Highlighting Hard and Imbalanced Samples, By Ao Zhou et al.
Multi-Label Adaptive Batch Selection by Highlighting Hard and Imbalanced Samples
by Ao Zhou, Bin Liu, Jin Wang, Grigorios Tsoumakas
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 explores the effectiveness of deep neural networks in classifying multi-label data from various domains, where traditional training modes may be biased towards majority labels due to class imbalance. The authors propose an adaptive batch selection algorithm tailored for multi-label deep learning models, prioritizing hard samples related to minority labels and incorporating informative label correlations. This approach is shown to converge faster and perform better than random batch selection across five multi-label models on 13 benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to help machines learn from lots of data that has many different categories or labels. Right now, these machines can be biased towards the most common labels because they don’t have a good way to deal with all the other labels. The researchers came up with a new way to pick which samples to use when training the machine, focusing on the harder-to-predict labels and taking into account how those labels are connected. They tested this method on 13 different datasets and found that it works better than just randomly picking samples. |
Keywords
* Artificial intelligence * Deep learning
