Summary of Neuro-mimetic Task-free Unsupervised Online Learning with Continual Self-organizing Maps, by Hitesh Vaidya et al.
Neuro-mimetic Task-free Unsupervised Online Learning with Continual Self-Organizing Maps
by Hitesh Vaidya, Travis Desell, Ankur Mali, Alexander Ororbia
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE)
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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 Continual SOM (CSOM) is an intelligent system capable of continual learning from potentially infinite streams of pattern vectors. The challenge lies in retaining previously acquired knowledge while learning from new samples, a problem known as catastrophic forgetting. While artificial neural networks (ANNs) have been extensively studied for forgetting, there is limited research on unsupervised architectures like the self-organizing map (SOM). Our results show that even SOMs can experience concept drift when processing continuous data streams. To address this, we propose a generalization of the SOM, CSOM, which achieves online unsupervised learning under a low memory budget. Our experiments on benchmarks like MNIST, Kuzushiji-MNIST, Fashion-MNIST, and CIFAR-10 demonstrate state-of-the-art results in online unsupervised class incremental learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about how machines can learn new things from lots of data without forgetting what they already know. This is a big problem because computers struggle to remember old things when they’re learning new ones. The researchers are trying to find ways to make it easier for computers to keep remembering and not forget. They look at an old way of doing this called the self-organizing map (SOM) and try to make it work better by creating a new version called the continual SOM (CSOM). They test their idea on some big datasets and find that it works really well, even better than what others have done before. |
Keywords
* Artificial intelligence * Continual learning * Generalization * Unsupervised
