Summary of A Survey Of Deep Long-tail Classification Advancements, by Charika De Alvis (the University Of Sydney et al.
A Survey of Deep Long-Tail Classification Advancements
by Charika de Alvis, Suranga Seneviratne
First submitted to arxiv on: 24 Apr 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 The paper tackles the challenge of learning from imbalanced data, which is common in real-world applications of deep learning. The authors argue that current state-of-the-art deep learning models require large volumes of training data, making it difficult to learn from skewed and long-tailed distributions. Despite progress in this area, standard benchmark datasets for classification still yield SOTA accuracies less than 75%. To address this issue, the paper provides a taxonomy of methods proposed for addressing the problem of long-tail classification, focusing on works from the last few years under a single mathematical framework. The authors also discuss performance metrics, convergence studies, feature distribution, and classifier analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about how to learn from data that’s not evenly spread out. This is a problem because most computer programs assume the data is evenly spread out. They need to find ways to fix this so they can work better with real-world data. The authors look at different methods people have used to solve this problem and see which ones work best. |
Keywords
» Artificial intelligence » Classification » Deep learning
