Summary of An Efficient Memory Module For Graph Few-shot Class-incremental Learning, by Dong Li et al.
An Efficient Memory Module for Graph Few-Shot Class-Incremental Learning
by Dong Li, Aijia Zhang, Junqi Gao, Biqing Qi
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposes Mecoin, an efficient method for building and maintaining memory in graph representation learning to tackle the problem of catastrophic forgetting. Traditional methods often rely on a large number of labels for node classification, which is impractical in real-world applications. The proposed method employs Structured Memory Units, Memory Construction Modules, and Memory Representation Adaptation Module to cache prototypes of learned categories, update these prototypes, and reduce the need for parameter fine-tuning. The model is evaluated through experiments and VC-dimension analysis, showing superior performance in accuracy and forgetting rate compared to other related works. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Mecoin is a new way to learn from graphs without forgetting what we already know. Normally, graph learning methods require lots of labeled data, which isn’t always available. Mecoin helps by remembering important patterns it’s learned before, so it can adapt quickly to new information. This makes it useful for real-world applications where we don’t have a lot of labeled data. |
Keywords
» Artificial intelligence » Classification » Fine tuning » Representation learning
