Summary of Towards Cross-domain Continual Learning, by Marcus De Carvalho et al.
Towards Cross-Domain Continual Learning
by Marcus de Carvalho, Mahardhika Pratama, Jie Zhang, Chua Haoyan, Edward Yapp
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 proposes an approach to continual learning that addresses the challenges of mastering a stream of tasks or classes without revisiting past data. The method leverages previously acquired knowledge to learn new tasks efficiently, while avoiding catastrophic forgetting. This is achieved by incorporating a meta-learning component into the model, which enables it to adapt to new tasks and retain information learned from previous tasks. The proposed approach demonstrates improved performance on benchmark datasets, including those from the Omniglot and MiniImagenet domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how machines can learn new things without forgetting what they already know. It’s a big challenge because if we train a machine to do one task well, but then it needs to do another task, it often forgets what it learned first. The paper suggests a way to solve this problem by using something called “meta-learning” that helps the machine learn from its past experiences and apply them to new tasks. This could be very useful in situations where we want machines to keep learning and improving over time. |
Keywords
* Artificial intelligence * Continual learning * Meta learning
