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Pruning (artificial neural network)

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In the context of artificial neural network, pruning is the practice of removing artificial neurons after learning, usually with the goal of reducing the computational resources required to run the neural network. A basic algorithm for pruning is as follows:[1][2]

  1. Evaluate the importance of each neuron.
  2. Rank the neurons according to their importance (assuming there is a clearly defined measure for "importance").
  3. Remove the least important neuron.
  4. Check a termination condition (to be determined by the user) to see whether to continue pruning.

References

  1. ^ Molchanov, P., Tyree, S., Karras, T., Aila, T., & Kautz, J. (2016). Pruning convolutional neural networks for resource efficient inference. arXiv preprint arXiv:1611.06440.
  2. ^ Pruning deep neural networks to make them fast and small.


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