Summary of Lotus: Improving Transformer Efficiency with Sparsity Pruning and Data Lottery Tickets, by Ojasw Upadhyay
LOTUS: Improving Transformer Efficiency with Sparsity Pruning and Data Lottery Tickets
by Ojasw Upadhyay
First submitted to arxiv on: 1 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper introduces a novel method called LOTUS (LOttery Transformers with Ultra Sparsity), which leverages data lottery ticket selection and sparsity pruning to accelerate vision transformer training while maintaining accuracy. The approach identifies the most informative data subsets, eliminates redundant model parameters, and optimizes the training process. Through extensive experiments, the authors demonstrate rapid convergence and high accuracy with significantly reduced computational requirements. The paper highlights the potential of combining data selection and sparsity techniques for efficient vision transformer training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to train computer models called vision transformers that are used in many applications like self-driving cars or medical imaging. Right now, these models take a long time to train because they need a lot of computing power. The authors came up with a solution called LOTUS that helps train these models faster while still getting good results. They did some experiments and found that their method works really well. This could help make self-driving cars or medical imaging machines work better in the future. |
Keywords
» Artificial intelligence » Pruning » Vision transformer
