Summary of Hyperparameters in Continual Learning: a Reality Check, by Sungmin Cha and Kyunghyun Cho
Hyperparameters in Continual Learning: A Reality Check
by Sungmin Cha, Kyunghyun Cho
First submitted to arxiv on: 14 Mar 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 research paper investigates the shortcomings of the conventional evaluation protocol for Continual Learning (CL) algorithms. The dominant approach selects the best hyperparameters within a scenario and then evaluates the algorithm using these hyperparameters in the same scenario, which overestimates the CL capacity and relies on unrealistic hyperparameter tuning. The authors argue that the evaluation should focus on assessing the generalizability of the CL capacity to unseen scenarios. To address this, they propose a revised two-phase evaluation protocol comprising a hyperparameter tuning phase and an evaluation phase, with different dataset configurations in each phase. This protocol is applied to class-incremental learning, both with and without pretrained models, yielding results that show most state-of-the-art algorithms fail to replicate their reported performance, highlighting the overestimation of CL capacity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper looks at how we test machine learning models that can learn from new data as it becomes available. Currently, we train these models on a specific set of tasks and then test them again on those same tasks. This is not very realistic because in real life, we don’t know what kind of new data will come along. The authors think this way of testing is flawed and suggest a new approach that involves training the model multiple times with different sets of data, to see how well it can handle unknown situations. |
Keywords
* Artificial intelligence * Continual learning * Hyperparameter * Machine learning
