Summary of Revised Regularization For Efficient Continual Learning Through Correlation-based Parameter Update in Bayesian Neural Networks, by Sanchar Palit et al.
Revised Regularization for Efficient Continual Learning through Correlation-Based Parameter Update in Bayesian Neural Networks
by Sanchar Palit, Biplab Banerjee, Subhasis Chaudhuri
First submitted to arxiv on: 21 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 proposed Bayesian neural network-based continual learning algorithm uses Variational Inference to overcome limitations in existing methods. It addresses challenges in storing network parameters while mitigating catastrophic forgetting, particularly when past datasets are limited. The method introduces a regularization term that targets the dynamics and population of parameter means and variances, as well as an importance-weighted Evidence Lower Bound term to capture data and parameter correlations. This enables effective storage of common and distinctive parameter hyperspace bases. Experimental results demonstrate superior performance compared to existing approaches across diverse datasets and various sequential dataset combinations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way for machines to learn from experience without forgetting what they already know. It’s like how humans remember things they learned in school, even if they don’t use that information every day. The method uses special math called Variational Inference to make sure the machine doesn’t forget important things it learned earlier. This is important because usually, machines have to store a lot of information about what they’ve learned, which takes up a lot of space. The new method makes it possible for machines to learn and remember without needing as much storage space. |
Keywords
» Artificial intelligence » Continual learning » Inference » Neural network » Regularization
