Summary of Addressing Loss Of Plasticity and Catastrophic Forgetting in Continual Learning, by Mohamed Elsayed et al.
Addressing Loss of Plasticity and Catastrophic Forgetting in Continual Learning
by Mohamed Elsayed, A. Rupam Mahmood
First submitted to arxiv on: 31 Mar 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 Deep representation learning methods face significant challenges when it comes to continual learning. Traditional methods either suffer from catastrophic forgetting of useful units or loss of plasticity due to rigid and unuseful units. This paper proposes Utility-based Perturbed Gradient Descent (UPGD) as a novel approach for the continual learning of representations. UPGD combines gradient updates with perturbations, applying smaller modifications to more useful units to protect them from forgetting, and larger modifications to less useful units to rejuvenate their plasticity. The proposed method is tested in a challenging streaming learning setup featuring hundreds of non-stationarities and unknown task boundaries. Results show that existing methods often suffer from decreasing accuracy over tasks, whereas UPGD continues to improve performance and outperforms or matches other methods in all problems. Additionally, the paper demonstrates the effectiveness of UPGD in extended reinforcement learning experiments with PPO, highlighting its ability to avoid performance drops after initial learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research focuses on helping machines learn new things while remembering what they already know. Current approaches often forget important information or struggle to adapt to new tasks. The authors propose a new method called Utility-based Perturbed Gradient Descent (UPGD) that helps machines remember and improve their learning over time. UPGD is tested in a challenging scenario where the machine must learn from multiple sources of data, without knowing what type of data it is or when changes occur. The results show that existing methods often fail to adapt to new tasks, but UPGD continues to learn and improves its performance. |
Keywords
* Artificial intelligence * Continual learning * Gradient descent * Reinforcement learning * Representation learning
