Summary of Any-way Meta Learning, by Junhoo Lee et al.
Any-Way Meta Learning
by Junhoo Lee, Yearim Kim, Hyunho Lee, Nojun Kwak
First submitted to arxiv on: 10 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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 “any-way” learning paradigm addresses the limitations of traditional meta-learning models by allowing them to adapt to tasks of varying cardinalities that were unseen during training. This is achieved through the use of stochastic numeric label assignments, which enables the model to learn from any possible way or combination of labels. The approach is demonstrated to outperform traditional fixed-way models in terms of performance, convergence speed, and stability on renowned architectures such as MAML and ProtoNet. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way of learning that lets machines adapt to different tasks more easily. Instead of being limited to one specific way of doing things, the “any-way” approach allows for flexibility and adaptation to new situations. This is achieved through the use of random labels, which enables the model to learn from any possible combination of labels. The results show that this approach can outperform traditional methods in terms of performance and speed. |
Keywords
* Artificial intelligence * Meta learning
