Summary of Dailymae: Towards Pretraining Masked Autoencoders in One Day, by Jiantao Wu et al.
DailyMAE: Towards Pretraining Masked Autoencoders in One Day
by Jiantao Wu, Shentong Mo, Sara Atito, Zhenhua Feng, Josef Kittler, Muhammad Awais
First submitted to arxiv on: 31 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 The paper proposes efficient training recipes for masked image modeling (MIM) self-supervised learning (SSL), focusing on mitigating data loading bottlenecks and employing progressive training techniques to maintain pretraining performance. The authors claim that their approach can achieve speed gains of up to 5.8 times, making high-efficiency SSL training feasible. This work aims to promote accessibility and advancement in SSL research, particularly for prototyping and initial testing of SSL ideas. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding ways to make self-supervised learning (SSL) faster and more efficient. Right now, it takes a lot of time and computing power to train these models, which makes it hard for researchers to test their ideas quickly. The authors came up with some new techniques that can speed up the training process by 5.8 times! This will make it easier for people to try out different SSL approaches and see what works best. |
Keywords
» Artificial intelligence » Pretraining » Self supervised
