Summary of Conml: a Universal Meta-learning Framework with Task-level Contrastive Learning, by Shiguang Wu et al.
ConML: A Universal Meta-Learning Framework with Task-Level Contrastive Learning
by Shiguang Wu, Yaqing Wang, Yatao Bian, Quanming Yao
First submitted to arxiv on: 8 Oct 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 universal meta-learning framework called ConML, which enables rapid adaptation to new tasks by leveraging task identity. The core of ConML is task-level contrastive learning, extending contrastive learning from the representation space to the model space in meta-learning. By minimizing inner-task distance and maximizing inter-task distance during meta-training, ConML integrates seamlessly with various meta-learning algorithms, resulting in performance improvements across diverse few-shot learning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a way for machines to learn quickly like humans do. It’s called ConML, and it lets machines adapt to new tasks without needing specific models or architectures. The main idea is to compare the outputs of the machine when trained on different parts of the same task or different tasks altogether. This helps the machine learn better and make fewer mistakes. The paper shows that this method works well with different types of learning, even in situations where machines only get a little bit of information before being tested. |
Keywords
» Artificial intelligence » Few shot » Meta learning
