Summary of Leveraging Hierarchical Taxonomies in Prompt-based Continual Learning, by Quyen Tran et al.
Leveraging Hierarchical Taxonomies in Prompt-based Continual Learning
by Quyen Tran, Hoang Phan, Minh Le, Tuan Truong, Dinh Phung, Linh Ngo, Thien Nguyen, Nhat Ho, Trung Le
First submitted to arxiv on: 6 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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 In this paper, researchers develop a new approach to mitigate catastrophic forgetting in Prompt-based Continual Learning (PCL) models by applying human habits of organizing and connecting information. They propose building a hierarchical tree structure based on expanding label sets to identify groups of similar classes that could cause confusion. Additionally, they explore hidden connections between classes using optimal transport-based approaches and develop a novel regularization loss function that encourages models to focus on challenging knowledge areas. Experimental results show significant superiority over state-of-the-art models on various benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to help computers learn better by making them think like humans do when we learn new things. Humans group similar ideas together in our brains, and this helps us remember what’s important. The researchers want to make computer models do the same thing. They create a special way of organizing information that helps computers focus on tricky topics and forget less as they learn new things. This makes them better at learning over time. |
Keywords
» Artificial intelligence » Continual learning » Loss function » Prompt » Regularization
