Summary of 94% on Cifar-10 in 3.29 Seconds on a Single Gpu, by Keller Jordan
94% on CIFAR-10 in 3.29 Seconds on a Single GPU
by Keller Jordan
First submitted to arxiv on: 30 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 This AI research paper introduces training methods for the widely used CIFAR-10 dataset, which enables researchers to accelerate experiments and reduce costs. The proposed methods achieve impressive accuracy levels of 94% in 3.29 seconds, 95% in 10.4 seconds, and 96% in 46.3 seconds when running on a single NVIDIA A100 GPU. To further improve training speeds, the authors propose a derandomized variant of horizontal flipping augmentation, which outperforms the standard method in every scenario where flipping is beneficial. The code for these methods is publicly released. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research makes it faster and cheaper to conduct machine learning experiments using a popular dataset called CIFAR-10. The scientists developed new ways to train models that work well on this data, allowing researchers to get results quickly. They also created a new way to flip images, which helps improve the training process. By sharing their code online, they hope to help others do similar research more efficiently. |
Keywords
» Artificial intelligence » Machine learning
