Summary of On the Convergence Of Continual Learning with Adaptive Methods, by Seungyub Han et al.
On the Convergence of Continual Learning with Adaptive Methods
by Seungyub Han, Yeongmo Kim, Taehyun Cho, Jungwoo Lee
First submitted to arxiv on: 8 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 investigates the concept of plasticity-stability dilemma in continual learning, specifically focusing on the convergence analysis of memory-based continual learning with stochastic gradient descent (SGD). The study reveals that training current tasks can cause cumulative degradation of previous tasks. To address this issue, the authors propose an adaptive method for non-convex continual learning (NCCL) that adjusts step sizes of both previous and current tasks based on gradients. The proposed algorithm achieves the same convergence rate as SGD when the catastrophic forgetting term is suppressed at each iteration. Experimental results demonstrate improved performance over existing methods in several image classification tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, researchers explore how our brains learn new skills without forgetting old ones. They found that training our brains to do something new can actually make it harder to remember old things. To fix this problem, the scientists developed a new way of learning called non-convex continual learning (NCCL). This method helps us learn new things while still remembering what we already know. The researchers tested their new approach on some image recognition tasks and found that it worked better than other methods. |
Keywords
» Artificial intelligence » Continual learning » Image classification » Stochastic gradient descent
