Structured pruning for deep convolutional neural networks: A survey
The remarkable performance of deep Convolutional neural networks (CNNs) is generally
attributed to their deeper and wider architectures, which can come with significant …
attributed to their deeper and wider architectures, which can come with significant …
Model compression for deep neural networks: A survey
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 …
been widely applied in various computer vision tasks. However, in the pursuit of …
A survey of quantization methods for efficient neural network inference
This chapter provides approaches to the problem of quantizing the numerical values in deep
Neural Network computations, covering the advantages/disadvantages of current methods …
Neural Network computations, covering the advantages/disadvantages of current methods …
Hrank: Filter pruning using high-rank feature map
Neural network pruning offers a promising prospect to facilitate deploying deep neural
networks on resource-limited devices. However, existing methods are still challenged by the …
networks on resource-limited devices. However, existing methods are still challenged by the …
Towards optimal structured cnn pruning via generative adversarial learning
Structured pruning of filters or neurons has received increased focus for compressing
convolutional neural networks. Most existing methods rely on multi-stage optimizations in a …
convolutional neural networks. Most existing methods rely on multi-stage optimizations in a …
Group fisher pruning for practical network compression
Network compression has been widely studied since it is able to reduce the memory and
computation cost during inference. However, previous methods seldom deal with …
computation cost during inference. However, previous methods seldom deal with …
Eagleeye: Fast sub-net evaluation for efficient neural network pruning
Finding out the computational redundant part of a trained Deep Neural Network (DNN) is the
key question that pruning algorithms target on. Many algorithms try to predict model …
key question that pruning algorithms target on. Many algorithms try to predict model …
Learning filter pruning criteria for deep convolutional neural networks acceleration
Filter pruning has been widely applied to neural network compression and acceleration.
Existing methods usually utilize pre-defined pruning criteria, such as Lp-norm, to prune …
Existing methods usually utilize pre-defined pruning criteria, such as Lp-norm, to prune …
Resrep: Lossless cnn pruning via decoupling remembering and forgetting
We propose ResRep, a novel method for lossless channel pruning (aka filter pruning), which
slims down a CNN by reducing the width (number of output channels) of convolutional …
slims down a CNN by reducing the width (number of output channels) of convolutional …
Cars: Continuous evolution for efficient neural architecture search
Searching techniques in most of existing neural architecture search (NAS) algorithms are
mainly dominated by differentiable methods for the efficiency reason. In contrast, we develop …
mainly dominated by differentiable methods for the efficiency reason. In contrast, we develop …