Structural neural network pruning aims to remove the redundant channels in the deep
convolutional neural networks (CNNs) by pruning the filters of less importance to the final …

Channel pruning is widely used to reduce the complexity of deep network models. Recent
pruning methods usually identify which parts of the network to discard by proposing a …

Achieving lightweight real-time object detection necessitates balancing model compression
with detection accuracy, a difficulty exacerbated by low redundancy and uneven …

The design and implementation of Deep Learning (DL) models is currently receiving a lot of
attention from both industrials and academics. However, the computational workload …

As large-scale pre-trained models have become the major choices of various applications,
new challenges arise for model pruning, eg, can we avoid pruning the same model from …

Structured pruning can simplify network architecture and improve inference speed.
Combined with the underlying hardware and inference engine in which the final model is …

Neural network pruning has remarkable performance for reducing the complexity of deep
network models. Recent network pruning methods usually focused on removing unimportant …

Deep neural networks (DNNs) have revolutionized various fields with their impressive
performance on particular tasks, but their increasing complexity raises concerns about …

Pruning plays an essential role in deploying deep neural nets (DNNs) to the hardware of
limited memory or computation. However, current high-quality iterative pruning can create a …