Summary of Toward Efficient Convolutional Neural Networks with Structured Ternary Patterns, by Christos Kyrkou
Toward Efficient Convolutional Neural Networks With Structured Ternary Patterns
by Christos Kyrkou
First submitted to arxiv on: 20 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 paper proposes a novel approach to designing convolutional neural networks (ConvNets) that are more efficient in terms of resource requirements. The method, called Structured Ternary Patterns (STePs), utilizes static convolutional filters generated from local binary patterns (LBPs) and Haar features. These filters require significantly less storage and can lead to inference improvements with proper low-level implementation. The proposed approach is validated using four image classification datasets, demonstrating that common network backbones can be made more efficient while providing competitive results. Furthermore, it is shown that custom STeP-based networks can provide good trade-offs for on-device applications such as unmanned aerial vehicle (UAV)-based aerial vehicle detection. The experimental results demonstrate a reduction in trainable parameters by 40-80% while maintaining high detection accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to make deep learning models more efficient so they can be used on devices with limited resources. The team proposes a new way to design convolutional neural networks (CNNs) that use less storage and processing power. They call this method “Structured Ternary Patterns” or STePs. This approach is tested using images of different types, showing that it can make existing models more efficient without sacrificing their performance. Additionally, the team shows how this method can be used to design custom CNNs for specific tasks, such as detecting objects in aerial vehicles. The results demonstrate a significant reduction in the amount of data needed to train these models while maintaining high accuracy. |
Keywords
» Artificial intelligence » Deep learning » Image classification » Inference
