Particle swarm optimization of deep neural networks architectures for image classification

By Francisco Erivaldo Fernandes Junior, Gary G. Yen. Swarm and Evolutionary Computation, 2019

343 citations recorded by the source corpus.

Abstract Deep neural networks have been shown to outperform classical machine learning algorithms in solving real-world problems. However, the most successful deep neural networks were handcrafted from scratch taking the problem domain knowledge into consideration. This approach often consumes very significant time and computational resources. In this work, we propose a novel algorithm based on particle swarm optimization (PSO), capable of fast convergence when compared with others evolutionary approaches, to automatically search for meaningful deep convolutional neural networks (CNNs) architectures for image classification tasks, named psoCNN. A novel directly encoding strategy and a velocity operator were devised allowing the optimization use of PSO with CNNs. Our experimental results show that psoCNN can quickly find good CNN architectures that achieve quality performance comparable to the state-of-the-art designs.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/b5cd5470d12e9b6d7eea3f243958789ec127c970

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

  1. Bing Xue
  2. Mengjie Zhang
  3. Yanan Sun
  4. Gary G. Yen
  5. Yuqiao Liu
  6. Yaochu Jin
  7. Handing Wang
  8. Ran Cheng

In Computer Vision and Pattern Recognition

  1. Bing Xue
  2. Mengjie Zhang
  3. Yanan Sun
  4. Gary G. Yen
  5. Yuqiao Liu
  6. Kate Saenko
  7. Gabriella Csurka
  8. Luc Van Gool

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