An Experimental Study of the Impact of Pre-training on the Pruning of a
Convolutional Neural Network
- URL: http://arxiv.org/abs/2112.08227v1
- Date: Wed, 15 Dec 2021 16:02:15 GMT
- Title: An Experimental Study of the Impact of Pre-training on the Pruning of a
Convolutional Neural Network
- Authors: Nathan Hubens, Matei Mancas, Bernard Gosselin, Marius Preda, Titus
Zaharia
- Abstract summary: In recent years, deep neural networks have known a wide success in various application domains.
Deep neural networks usually involve a large number of parameters, which correspond to the weights of the network.
The pruning methods notably attempt to reduce the size of the parameter set, by identifying and removing the irrelevant weights.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In recent years, deep neural networks have known a wide success in various
application domains. However, they require important computational and memory
resources, which severely hinders their deployment, notably on mobile devices
or for real-time applications. Neural networks usually involve a large number
of parameters, which correspond to the weights of the network. Such parameters,
obtained with the help of a training process, are determinant for the
performance of the network. However, they are also highly redundant. The
pruning methods notably attempt to reduce the size of the parameter set, by
identifying and removing the irrelevant weights. In this paper, we examine the
impact of the training strategy on the pruning efficiency. Two training
modalities are considered and compared: (1) fine-tuned and (2) from scratch.
The experimental results obtained on four datasets (CIFAR10, CIFAR100, SVHN and
Caltech101) and for two different CNNs (VGG16 and MobileNet) demonstrate that a
network that has been pre-trained on a large corpus (e.g. ImageNet) and then
fine-tuned on a particular dataset can be pruned much more efficiently (up to
80% of parameter reduction) than the same network trained from scratch.
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