Summary of Towards Continuous Reuse Of Graph Models Via Holistic Memory Diversification, by Ziyue Qiao et al.
Towards Continuous Reuse of Graph Models via Holistic Memory Diversification
by Ziyue Qiao, Junren Xiao, Qingqiang Sun, Meng Xiao, Xiao Luo, Hui Xiong
First submitted to arxiv on: 11 Jun 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 tackles the challenge of incremental learning in growing graphs with increasingly complex tasks. The goal is to continuously train a graph model to handle new tasks while retaining proficiency in previous tasks via memory replay. Existing methods overlook the importance of memory diversity, limiting their ability to select high-quality memories from previous tasks and remember broad previous knowledge within the scarce memory on graphs. To address this, we introduce a novel holistic Diversified Memory Selection and Generation (DMSG) framework for incremental learning in graphs, which first introduces a buffer selection strategy that considers both intra-class and inter-class diversities, employing an efficient greedy algorithm for sampling representative training nodes from graphs into memory buffers after learning each new task. Then, to adequately rememorize the knowledge preserved in the memory buffer when learning new tasks, a diversified memory generation replay method is introduced. This method utilizes a variational layer to generate the distribution of buffer node embeddings and sample synthesized ones for replaying. Furthermore, an adversarial variational embedding learning method and a reconstruction-based decoder are proposed to maintain the integrity and consolidate the generalization of the synthesized node embeddings, respectively. Extensive experimental results on publicly accessible datasets demonstrate the superiority of our method over state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about helping computers learn new things without forgetting what they already know. This is important because it can help us build more powerful artificial intelligence systems that can get better at doing tasks over time. The authors introduce a new way to do this called DMSG, which helps the computer pick the most important memories from previous learning experiences and use them to improve its performance on new tasks. |
Keywords
» Artificial intelligence » Decoder » Embedding » Generalization
