Summary of Wash: Train Your Ensemble with Communication-efficient Weight Shuffling, Then Average, by Louis Fournier (mlia) et al.
WASH: Train your Ensemble with Communication-Efficient Weight Shuffling, then Average
by Louis Fournier, Adel Nabli, Masih Aminbeidokhti, Marco Pedersoli, Eugene Belilovsky, Edouard Oyallon
First submitted to arxiv on: 27 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Neural and Evolutionary Computing (cs.NE); Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 novel paper introduces WASH, a distributed method for training model ensembles that achieves state-of-the-art image classification accuracy. The method leverages weight averaging techniques to balance generalization and inference speed, while addressing the challenges of aligning models to improve performance. By randomly shuffling a small percentage of weights during training, WASH maintains diverse models within the same basin, reducing communication costs compared to standard parameter averaging methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary WASH is a new way to train many models at once that works well for image classification tasks. Normally, when you average the outputs of multiple models, it helps them work better together, but it can be slow and expensive. WASH fixes this problem by allowing the models to learn from each other during training, which makes them more accurate and efficient. |
Keywords
» Artificial intelligence » Generalization » Image classification » Inference
