Summary of Self-guided Masked Autoencoders For Domain-agnostic Self-supervised Learning, by Johnathan Xie et al.
Self-Guided Masked Autoencoders for Domain-Agnostic Self-Supervised Learning
by Johnathan Xie, Yoonho Lee, Annie S. Chen, Chelsea Finn
First submitted to arxiv on: 22 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 Self-supervised learning excels at learning representations from large amounts of unlabeled data, demonstrating success across multiple data modalities. The paper presents Self-guided Masked Autoencoders (SMA), a fully domain-agnostic masked modeling method that trains an attention-based model using a masked modeling objective without relying on input augmentations or domain-specific assumptions. SMA is evaluated on three self-supervised learning benchmarks in protein biology, chemical property prediction, and particle physics, achieving state-of-the-art performance. The paper’s contributions include a new approach to masked modeling that does not rely on domain-specific knowledge, allowing for the extension of self-supervised learning to new modalities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SMA is a way to learn things from lots of data without labels. It’s like teaching someone new things just by showing them examples. The problem is, most methods are only good at one type of data. SMA tries to fix this by not relying on any special tricks for each type of data. It does this by using something called “masked modeling” which means it hides parts of the data and then tries to fill in what’s missing. This helps it learn general things that can be used with lots of different types of data. SMA was tested on three different areas: biology, chemistry, and physics. It did really well in all three areas! |
Keywords
* Artificial intelligence * Attention * Self supervised
