Summary of Examining Changes in Internal Representations Of Continual Learning Models Through Tensor Decomposition, by Nishant Suresh Aswani et al.
Examining Changes in Internal Representations of Continual Learning Models Through Tensor Decomposition
by Nishant Suresh Aswani, Amira Guesmi, Muhammad Abdullah Hanif, Muhammad Shafique
First submitted to arxiv on: 6 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposes a novel evaluation framework for Continual Learning (CL) models, focusing on representational forgetting within the model. The framework involves gathering internal representations throughout the learning process and forming three-dimensional tensors. By analyzing these tensors using Tensor Component Analysis (TCA), the authors aim to uncover patterns about how internal representations evolve. The approach is applied across different model architectures and importance-based CL strategies, with a curated task selection. While the results mirror the difference in performance of various CL strategies, the methodology did not directly highlight specialized clusters of neurons or provide immediate understanding of filter evolution. This study contributes to the evaluation of CL models, providing insights into their benefits and pitfalls. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine your brain is learning new things every day. How do you remember what you learned yesterday? That’s what this paper is about – how we can make machines learn new things while remembering what they already know. The researchers created a new way to look at the machine’s “thoughts” (called internal representations) as it learns and forgets. They used special math called Tensor Component Analysis to see how these thoughts change over time. By looking at many different types of machines and ways they learn, the researchers found that this method can help us understand which approaches are best for making machines learn new things while keeping what they already know. |
Keywords
* Artificial intelligence * Continual learning
