Summary of Disentangling and Mitigating the Impact Of Task Similarity For Continual Learning, by Naoki Hiratani
Disentangling and Mitigating the Impact of Task Similarity for Continual Learning
by Naoki Hiratani
First submitted to arxiv on: 30 May 2024
Categories
- Main: Machine Learning (stat.ML)
- 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 This paper addresses the challenge of continual learning for artificial neural networks when faced with partially similar tasks. The researchers develop a linear teacher-student model with latent structure and analyze its performance in scenarios with varying input feature and readout similarity. They find that high input feature similarity coupled with low readout similarity leads to catastrophic forgetting, while the opposite scenario is more benign. Additionally, they explore different algorithms for continual learning, including task-dependent activity gating, task-dependent plasticity gating, and weight regularization based on the Fisher information metric. The results demonstrate that certain approaches can improve knowledge retention at the expense of transfer or vice versa. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper studies how artificial neural networks learn when faced with similar tasks. The researchers look at how different aspects of these tasks affect learning, such as what’s in the input and how it’s processed. They find that if the inputs are very similar but the processing is different, it can be hard for the network to learn. But if the inputs are different and the processing is similar, it’s easier. The researchers also test different ways for the network to learn, like adjusting its activity or flexibility based on the task. They find that some approaches help the network remember what it learned, while others let it transfer that learning to new tasks. |
Keywords
» Artificial intelligence » Continual learning » Regularization » Student model
