Summary of Is Continual Learning Ready For Real-world Challenges?, by Theodora Kontogianni et al.
Is Continual Learning Ready for Real-world Challenges?
by Theodora Kontogianni, Yuanwen Yue, Siyu Tang, Konrad Schindler
First submitted to arxiv on: 15 Feb 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 highlights the discrepancy between the challenges of continual learning and current evaluation protocols, leading to ineffective solutions for real-world scenarios. The authors propose a new 3D semantic segmentation benchmark, OCL-3DSS, to assess various continual learning schemes from the literature. By using more realistic protocols that require online and continual learning in dynamic settings (e.g., robotics and 3D vision applications), the study finds that all considered methods perform poorly, deviating significantly from the upper bound of joint offline training. This raises questions about the applicability of existing methods in realistic settings. The paper aims to initiate a paradigm shift by advocating for the adoption of continual learning methods through new experimental protocols that better emulate real-world conditions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at why we’re not using continual learning as much as we could be. It says that current ways of testing this type of learning are too simple and don’t match what happens in real life. The authors created a new way to test 3D image segmentation, which is like trying to find specific objects in a picture. They then tested different methods for continual learning using this new test, and found that none of them work very well when used in real-life situations. This makes the authors wonder if we should be looking at ways to make these methods work better in the real world. |
Keywords
* Artificial intelligence * Continual learning * Image segmentation * Semantic segmentation
