Summary of Interactive Continual Learning: Fast and Slow Thinking, by Biqing Qi et al.
Interactive Continual Learning: Fast and Slow Thinking
by Biqing Qi, Xingquan Chen, Junqi Gao, Dong Li, Jianxing Liu, Ligang Wu, Bowen Zhou
First submitted to arxiv on: 5 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 proposed Interactive Continual Learning (ICL) framework leverages collaborative interactions among models of various sizes to achieve continual learning. The authors draw inspiration from Complementary Learning System theory, employing a ViT model as System1 and multimodal LLM as System2. To enable memory modules to deduce tasks from class information and enhance Set2Set retrieval, the Class-Knowledge-Task Multi-Head Attention (CKT-MHA) is proposed. Additionally, the CL-vMF mechanism, based on von Mises-Fisher (vMF) distribution, enhances geometric representation in System1’s memory module. The vMF-ODI strategy identifies hard examples, promoting collaboration between System1 and System2 for complex reasoning realization. Evaluations demonstrate significant resistance to forgetting and superior performance compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way for machines to learn continuously by working together with other models. It uses a combination of different models and techniques to help each model remember what it has learned and forget less important information. This allows the machine learning system to get better at solving problems over time, even if the type of problem or the data changes. |
Keywords
* Artificial intelligence * Continual learning * Machine learning * Multi head attention * Vit
