Summary of Efficient Deep Learning Board: Training Feedback Is Not All You Need, by Lina Gong et al.
Efficient Deep Learning Board: Training Feedback Is Not All You Need
by Lina Gong, Qi Gao, Peng Li, Mingqiang Wei, Fei Wu
First submitted to arxiv on: 17 Oct 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 proposed EfficientDL framework aims to address the limitations of current automatic deep learning (AutoDL) frameworks by providing quick and accurate performance predictions for selecting suitable DL systems without requiring any training feedback. The innovative approach relies on a comprehensive, multi-dimensional, fine-grained system component dataset that enables the development of a static performance prediction model and an optimized component recommendation algorithm. This compatibility with mainstream models like ResNet50, MobileNetV3, EfficientNet-B0, MaxViT-T, Swin-B, and DaViT-T brings competitive performance improvements. Experimental results on the CIFAR-10 dataset show that EfficientDL outperforms existing AutoML tools in both accuracy (1.31% Top-1 improvement) and efficiency (approximately 20 times faster). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary EfficientDL is a new way to predict how well deep learning models will work without having to train them first. This is helpful because it can take a long time and lots of computer power to train these models. The developers made a big list of all the different parts that make up a deep learning model, which helps their system predict how well a model will work without training it. This means you can pick the best model for your task more quickly and easily. They tested this system on a popular dataset called CIFAR-10 and found that it works better than other similar systems. |
Keywords
* Artificial intelligence * Deep learning
