Summary of An Efficient Nas-based Approach For Handling Imbalanced Datasets, by Zhiwei Yao
An Efficient NAS-based Approach for Handling Imbalanced Datasets
by Zhiwei Yao
First submitted to arxiv on: 22 Jun 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 This novel approach optimizes the backbone architecture through neural architecture search (NAS) to enhance performance on long-tailed datasets, addressing class imbalance issues common in real-world data distributions. The study shows that accuracy on balanced datasets does not reliably predict performance on imbalanced datasets, necessitating a complete NAS run on long-tailed datasets, which can be computationally expensive. To address this challenge, the research focuses on IMB-NAS, an efficient adaptation strategy retraining the linear classification head with reweighted loss while keeping the backbone NAS super-network frozen. Experiments on the imbalanced CIFAR dataset evaluate performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a way to make computer models more accurate when they’re faced with unbalanced data. Imagine you have a model that’s really good at identifying dogs, but it can’t recognize cats because there are so few pictures of cats in its training set. This problem is common and can be a big issue for machine learning. The researchers tried something new to solve this problem, called IMB-NAS, which lets them take an existing model trained on balanced data and adapt it to work well with unbalanced data. They tested their approach on a popular dataset of images and found that it really works. |
Keywords
» Artificial intelligence » Classification » Machine learning
