Summary of Lightcl: Compact Continual Learning with Low Memory Footprint For Edge Device, by Zeqing Wang et al.
LightCL: Compact Continual Learning with Low Memory Footprint For Edge Device
by Zeqing Wang, Fei Cheng, Kangye Ji, Bohu Huang
First submitted to arxiv on: 15 Jul 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 This paper proposes a compact algorithm called LightCL, which enables neural networks to continually learn and adapt to dynamic surroundings with reduced resource consumption. Unlike traditional CL methods that require extensive training for generalizability, LightCL evaluates and compresses the redundancy of already generalized components in the network’s structure. The algorithm considers two key factors: learning plasticity and memory stability, designing metrics to quantify the neural network’s generalizability during CL. Experiments show that LightCL outperforms state-of-the-art methods while reducing memory footprint by up to 6.16 times. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LightCL is a new way for computers to learn and adapt to changing situations. Most computer programs need lots of training data to be good at many tasks, but this takes up too much space or power. LightCL makes neural networks more efficient by identifying parts that are already good at certain tasks and leaving them alone. It also helps the network remember important patterns it has learned before, which makes it better at new tasks without needing as much training data. This means computers can learn and adapt faster and use less memory. |
Keywords
» Artificial intelligence » Neural network
