Summary of Evolutionary Neural Architecture Search For 3d Point Cloud Analysis, by Yisheng Yang et al.
Evolutionary Neural Architecture Search for 3D Point Cloud Analysis
by Yisheng Yang, Guodong Du, Chean Khim Toa, Ho-Kin Tang, Sim Kuan Goh
First submitted to arxiv on: 10 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
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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 A novel approach to automating the design of neural networks is proposed in this paper, which leverages optimization algorithms to navigate complex architecture spaces. This technique, known as Success-History-based Self-adaptive Differential Evolution with a Joint Point Interaction Dimension Search (SHSADE-PIDS), enables the discovery of efficient architectures for analyzing unstructured 3D point clouds. By encoding discrete deep neural network architectures in continuous spaces and performing searches in these spaces, SHSADE-PIDS can efficiently discover architectures that excel in tasks such as 3D segmentation and classification. Experimental results on challenging benchmarks demonstrate the capabilities of this approach, achieving state-of-the-art performance on SemanticKITTI and ModelNet40 while significantly reducing computational overhead. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses a special computer program to help design better neural networks for analyzing 3D point clouds. Neural networks are like super powerful computers that can be trained to do lots of tasks. But designing them can be really hard, so scientists have developed a way to use an optimization algorithm to find the best design. This new approach is called SHSADE-PIDS and it works by taking the possible designs and searching through them until it finds one that does well on a task. It was tested on some very challenging problems and did better than other approaches, which means it can be used to make computers even more powerful and accurate. |
Keywords
* Artificial intelligence * Classification * Neural network * Optimization
