Summary of Differentiable Model Scaling Using Differentiable Topk, by Kai Liu et al.
Differentiable Model Scaling using Differentiable Topk
by Kai Liu, Ruohui Wang, Jianfei Gao, Kai Chen
First submitted to arxiv on: 12 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 This study proposes Differentiable Model Scaling (DMS), a novel approach to automate neural architecture search for optimal network width and depth. Unlike existing NAS methods, DMS efficiently optimizes both width and depth in a fully differentiable manner, allowing for direct optimization. The authors evaluate DMS on various tasks, including image classification, object detection, and language modeling, using diverse network architectures like CNNs and Transformers. Results show that DMS outperforms state-of-the-art NAS methods, achieving improved performance and reduced search times. Specifically, DMS improves the top-1 accuracy of EfficientNet-B0 and Deit-Tiny by 1.4% and 0.6%, respectively, on ImageNet, while requiring only 0.4 GPU days for searching. The study’s findings have implications for scaling networks in various applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to make artificial intelligence models better. Currently, people design these models by hand, which can be inefficient. Researchers have come up with a way called Neural Architecture Search (NAS) that tries to find the best model automatically. But NAS has its own problems. This study introduces a new method called Differentiable Model Scaling (DMS), which makes it easier and faster to find better models. The authors test their method on different tasks, like recognizing objects in pictures or understanding natural language. They show that DMS can improve performance and reduce the time it takes to find good models. |
Keywords
» Artificial intelligence » Image classification » Object detection » Optimization
