The remarkable performance of deep Convolutional neural networks (CNNs) is generally
attributed to their deeper and wider architectures, which can come with significant …

Currently, with the rapid development of deep learning, deep neural networks (DNNs) have
been widely applied in various computer vision tasks. However, in the pursuit of …

Practitioners frequently observe that pruning improves model generalization. A long-
standing hypothesis based on bias-variance trade-off attributes this generalization …

In this paper, we propose a novel layer-adaptive weight-pruning approach for Deep Neural
Networks (DNNs) that addresses the challenge of optimizing the output distortion …

Neural networks tend to achieve better accuracy with training if they are larger {—} even if
the resulting models are overparameterized. Nevertheless, carefully removing such excess …

The existing model compression methods via structured pruning typically require
complicated multi-stage procedures. Each individual stage necessitates numerous …

Deep neural networks (DNNs) have made significant achievements in a wide variety of
domains. For the deep learning tasks, multiple excellent hardware platforms provide efficient …

Abstract Model compression is an essential technique for deploying deep neural networks
(DNNs) on power and memory-constrained resources. However, existing model …

Current techniques for deep neural network (DNN) pruning often involve intricate multi-step
processes that require domain-specific expertise making their widespread adoption …

Pruning at initialization (PaI) aims to remove weights of neural networks before training in
pursuit of training efficiency besides the inference. While off-the-shelf PaI methods manage …