Summary of Online Analytic Exemplar-free Continual Learning with Large Models For Imbalanced Autonomous Driving Task, by Huiping Zhuang et al.
Online Analytic Exemplar-Free Continual Learning with Large Models for Imbalanced Autonomous Driving Task
by Huiping Zhuang, Di Fang, Kai Tong, Yuchen Liu, Ziqian Zeng, Xu Zhou, Cen Chen
First submitted to arxiv on: 28 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Robotics (cs.RO)
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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 Analytic Exemplar-Free Online Continual Learning algorithm (AEF-OCL) tackles two major challenges in online continual learning: catastrophic forgetting and data imbalance. The AEF-OCL leverages analytic continual learning principles, employing ridge regression as a classifier for features extracted by a large backbone network. This approach ensures an equalization between the continual learning and its joint-learning counterpart without requiring any exemplar storage. To address data imbalance, the AEF-OCL introduces a Pseudo-Features Generator (PFG) module that recursively estimates the mean and variance of real features for each class. Experimental results demonstrate that the proposed method outperforms various methods on the autonomous driving SODA10M dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to learn online, called Analytic Exemplar-Free Online Continual Learning. This means that even when we get new information, our model can still be good at recognizing things it saw before. The method uses special math to make sure the model doesn’t forget what it learned earlier and also deals with situations where some classes have a lot more data than others. The authors tested their method on a big dataset of autonomous driving and found that it worked better than other methods. |
Keywords
» Artificial intelligence » Continual learning » Regression
