Summary of Densely Distilling Cumulative Knowledge For Continual Learning, by Zenglin Shi et al.
Densely Distilling Cumulative Knowledge for Continual Learning
by Zenglin Shi, Pei Liu, Tong Su, Yunpeng Wu, Kuien Liu, Yu Song, Meng Wang
First submitted to arxiv on: 16 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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 The proposed Dense Knowledge Distillation (DKD) method addresses the issue of catastrophic forgetting in continual learning by developing a novel distillation approach that leverages a task pool to track the model’s capabilities. DKD partitions the output logits into dense groups, each corresponding to a task in the task pool, and distills all tasks’ knowledge using all groups. To mitigate computational costs, random group selection is suggested in each optimization step. Additionally, an adaptive weighting scheme balances the learning of new classes with the retention of old classes based on class count and similarity. Experimental results demonstrate DKD’s superiority over recent state-of-the-art baselines across diverse benchmarks and scenarios, showcasing its ability to enhance model stability, promote flatter minima for improved generalization, and remain robust across various memory budgets and task orders. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DKD is a new way of learning that helps machines remember what they’ve learned before. It’s like having a special box where you keep all the things you’ve learned, so you can use them again later. DKD makes sure this box stays full by learning from all the tasks it’s been trained on, and it even has a special trick to make sure it doesn’t forget what it already knew. This helps machines learn faster and better, and it works well with other ways of learning that are popular right now. |
Keywords
» Artificial intelligence » Continual learning » Distillation » Generalization » Knowledge distillation » Logits » Optimization
