Summary of Continual Learning Using a Kernel-based Method Over Foundation Models, by Saleh Momeni et al.
Continual Learning Using a Kernel-Based Method Over Foundation Models
by Saleh Momeni, Sahisnu Mazumder, Bing Liu
First submitted to arxiv on: 20 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); 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 addresses the challenges of class-incremental learning (CIL) in continual learning, where a model learns a sequence of tasks incrementally. The proposed method, Kernel Linear Discriminant Analysis (KLDA), leverages features learned from a foundation model and incorporates Radial Basis Function (RBF) kernel and Random Fourier Features (RFF) to enhance feature representations. KLDA updates a shared covariance matrix for all learned classes based on the kernelized features, allowing it to classify new tasks effectively. The paper evaluates KLDA using text and image classification datasets, demonstrating significant performance improvements over baselines. Notably, KLDA achieves accuracy comparable to joint training of all classes without relying on replay data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about how machines can learn new things as they go along. It’s called class-incremental learning. The problem is that when a machine learns something new, it often forgets what it learned before. This paper proposes a new way to do class-incremental learning that avoids this forgetting and helps the machine learn better. It uses a special kind of math to make sure the machine doesn’t forget what it already knows. The researchers tested their method on different types of data, like text and images, and found that it worked really well. |
Keywords
» Artificial intelligence » Continual learning » Image classification
