Summary of Efficiency For Free: Ideal Data Are Transportable Representations, by Peng Sun et al.
Efficiency for Free: Ideal Data Are Transportable Representations
by Peng Sun, Yi Jiang, Tao Lin
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 investigates the efficiency properties of data in machine learning from both optimization and generalization perspectives. It reveals that using a publicly available, task-agnostic model as a prior model can produce efficient data, accelerating representation learning. The authors propose the Representation Learning Accelerator (ReLA) to promote the formation and utilization of efficient data. Experimental results show that utilizing ReLA reduces computational costs by 50% while maintaining accuracy on ImageNet-1K. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how data affects machine learning efficiency. It finds that using a prior model, like ResNet-18 pre-trained on CIFAR-10, can help train ResNet-50 on ImageNet-1K faster and cheaper without sacrificing accuracy. This is important because it could make AI more efficient and cost-effective. |
Keywords
» Artificial intelligence » Generalization » Machine learning » Optimization » Representation learning » Resnet
