Summary of Accelerating Augmentation Invariance Pretraining, by Jinhong Lin et al.
Accelerating Augmentation Invariance Pretraining
by Jinhong Lin, Cheng-En Wu, Yibing Wei, Pedro Morgado
First submitted to arxiv on: 27 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 Our research focuses on addressing the computational challenges associated with contrastive learning methods for pretraining Vision Transformers (ViTs). Despite the effectiveness of these methods, the substantial computational resources required often hinder their practical application. To mitigate this issue, we propose an acceleration framework that leverages ViT’s unique ability to generalize across inputs of varying sequence lengths. Our method employs a mix of sequence compression strategies, including randomized token dropout and flexible patch scaling, to reduce the cost of gradient estimation and accelerate convergence. We also provide an in-depth analysis of the gradient estimation error of various acceleration strategies as well as their impact on downstream tasks, offering valuable insights into the trade-offs between acceleration and performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to learn from a huge library with millions of books. That’s kind of like what computers are doing when they’re learning from lots of pictures or videos. But right now, this process takes too much time and computer power. Our team has found a way to speed up this process by using special techniques that help the computer understand how to look at different kinds of information. We also tested these new methods on real-world tasks like recognizing objects in pictures and found that they work well. |
Keywords
» Artificial intelligence » Dropout » Pretraining » Token » Vit
