Summary of Reflecting on the State Of Rehearsal-free Continual Learning with Pretrained Models, by Lukas Thede et al.
Reflecting on the State of Rehearsal-free Continual Learning with Pretrained Models
by Lukas Thede, Karsten Roth, Olivier J. Hénaff, Matthias Bethge, Zeynep Akata
First submitted to arxiv on: 13 Jun 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 investigates the relationship between methodological choices in continual learning (CL) and their reported high benchmark scores. The study focuses on perpetual adaptation of pre-trained models, which has achieved success on rehearsal-free CL benchmarks. Most proposed methods adapt parameter-efficient finetuning techniques to suit the continual nature of the problem. However, critical studies have highlighted competitive results by training on just the first task or via simple non-parametric baselines. This work tackles these questions and better understands the true drivers behind strong performances in perpetual adaptation. The study shows that P-RFCL techniques relying on input-conditional query mechanisms work not because of them but rather despite them, collapsing towards standard PEFT shortcut solutions. Furthermore, it identifies an implicit bound on tunable parameters when deriving RFCL approaches from PEFT methods as a potential denominator behind P-RFCL efficacy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well computers learn new things by adapting to new tasks without starting over. The researchers found that many people have been using the same method to do this, which works okay but not amazingly well. They also discovered that some simple methods actually work just as well or even better than the more complex ones. The study tries to figure out why this is and what it means for how computers learn. |
Keywords
* Artificial intelligence * Continual learning * Parameter efficient
