Summary of The Solution For the Sequential Task Continual Learning Track Of the 2nd Greater Bay Area International Algorithm Competition, by Sishun Pan et al.
The Solution for the sequential task continual learning track of the 2nd Greater Bay Area International Algorithm Competition
by Sishun Pan, Xixian Wu, Tingmin Li, Longfei Huang, Mingxu Feng, Zhonghua Wan, Yang Yang
First submitted to arxiv on: 6 Jul 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 This paper proposes a novel continual learning algorithm that excels in sequential tasks without requiring additional data or network modifications. The method isolates parameters for each task within the convolutional and linear layers, freezing batch normalization layers after the first task. For domain incremental settings, it freezes the shared classification head to prevent catastrophic forgetting. Additionally, the approach introduces strategies for inference task identity selection, gradient supplementation, adaptive importance scoring, and mask matrix compression. These innovations enable effective learning for new tasks without expanding the core network or using external data. The solution won a second-place prize in the 2nd Greater Bay Area International Algorithm Competition. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates an easy way to learn new things when you have many small tasks. It’s like having a special tool that helps your brain remember each task separately, so you don’t forget what you learned before. This is helpful because it means you can keep learning and improving without getting confused or stuck. The idea is to freeze certain parts of the brain after you learn something new, so you don’t accidentally erase what you already know. It also has special tricks to help you figure out which task you’re doing now, and how to make sure you’re focusing on the right one. This approach won a prize in a competition because it works well and is efficient. |
Keywords
» Artificial intelligence » Batch normalization » Classification » Continual learning » Inference » Mask
