Network Pruning
Network pruning is a technique used in deep learning to reduce the size of a neural network by removing unnecessary connections and parameters without significantly impacting its accuracy. The goal of network pruning is to create a smaller and more efficient neural network that can be deployed on resource-constrained devices such as mobile phones and embedded systems. Network pruning can be done in different ways, such as weight pruning, neuron pruning, and filter pruning. Weight pruning involves removing the connections with the smallest weights, while neuron pruning involves removing entire neurons that have little impact on the output. Filter pruning involves removing entire filters from convolutional layers that have little impact on the output. Network pruning can be done during training or after training, and can be combined with other techniques such as quantization and knowledge distillation to further improve the efficiency of the neural network.
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