In the current era of multi-modal and large models gradually revealing their potential, neural
network pruning has emerged as a crucial means of model compression. It is widely …

In recent years, deep network pruning has attracted significant attention in order to enable
the rapid deployment of AI into small devices with computation and memory constraints …

Network pruning is a widely-used compression technique that is able to significantly scale
down overparameterized models with minimal loss of accuracy. This paper shows that …

Pruning has emerged as a promising approach for compressing large-scale models, yet its
effectiveness in recovering the sparsest of models has not yet been explored. We conducted …

To show the efficacy of the proposed model we leverage three standard architectures VGG-
19 [5], ResNet-32 [1] and ResNet-50 [1]. We use these architecture on the small scale …

Modern neural networks are often massively overparameterized leading to high compute
costs during training and at inference. One effective method to improve both the compute …

The recent focus on the efficiency of deep neural networks (DNNs) has led to significant
work on model compression approaches, of which weight pruning is one of the most …

Densely structured pruning methods—which generate pruned models in a fully dense
format, allowing immediate compression benefits without additional demands—are evolving …

Abstract In recent years, Deep Neural Networks (DNNs) have significantly advanced the
state-of-the-art in numerous machine learning tasks. Unfortunately, most compact devices …

The model pruning algorithms apply different standards or methods to prune the redundant
neurons in the deep neural network, which can compress the model to the maximum extent …