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Channel Pruning for Accelerating Very Deep Neural Networks
Yihui He,Xiangyu Zhang,Jian Sun +2 more
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In this article, a LASSO regression based channel selection and least square reconstruction is proposed to accelerate very deep convolutional neural networks, which reduces the accumulated error and enhances the compatibility with various architectures.Abstract:
In this paper, we introduce a new channel pruning method to accelerate very deep convolutional neural networks.Given a trained CNN model, we propose an iterative two-step algorithm to effectively prune each layer, by a LASSO regression based channel selection and least square reconstruction. We further generalize this algorithm to multi-layer and multi-branch cases. Our method reduces the accumulated error and enhance the compatibility with various architectures. Our pruned VGG-16 achieves the state-of-the-art results by 5x speed-up along with only 0.3% increase of error. More importantly, our method is able to accelerate modern networks like ResNet, Xception and suffers only 1.4%, 1.0% accuracy loss under 2x speed-up respectively, which is significant. Code has been made publicly available.read more
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Book ChapterDOI
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
TL;DR: ShuffleNet V2 as discussed by the authors proposes to evaluate the direct metric on the target platform, beyond only considering FLOPs, based on a series of controlled experiments, and derives several practical guidelines for efficient network design.
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Rethinking the Value of Network Pruning
TL;DR: It is found that with optimal learning rate, the "winning ticket" initialization as used in Frankle & Carbin (2019) does not bring improvement over random initialization, and the need for more careful baseline evaluations in future research on structured pruning methods is suggested.
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A Survey of Model Compression and Acceleration for Deep Neural Networks
TL;DR: This paper survey the recent advanced techniques for compacting and accelerating CNNs model developed, roughly categorized into four schemes: parameter pruning and sharing, low-rank factorization, transferred/compact convolutional filters, and knowledge distillation.
Proceedings ArticleDOI
GhostNet: More Features From Cheap Operations
Abstract: Deploying convolutional neural networks (CNNs) on embedded devices is difficult due to the limited memory and computation resources. The redundancy in feature maps is an important characteristic of those successful CNNs, but has rarely been investigated in neural architecture design. This paper proposes a novel Ghost module to generate more feature maps from cheap operations. Based on a set of intrinsic feature maps, we apply a series of linear transformations with cheap cost to generate many ghost feature maps that could fully reveal information underlying intrinsic features. The proposed Ghost module can be taken as a plug-and-play component to upgrade existing convolutional neural networks. Ghost bottlenecks are designed to stack Ghost modules, and then the lightweight GhostNet can be easily established. Experiments conducted on benchmarks demonstrate that the proposed Ghost module is an impressive alternative of convolution layers in baseline models, and our GhostNet can achieve higher recognition performance (e.g. 75.7% top-1 accuracy) than MobileNetV3 with similar computational cost on the ImageNet ILSVRC-2012 classification dataset. Code is available at https://github.com/huawei-noah/ghostnet.
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The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
Jonathan Frankle,Michael Carbin +1 more
TL;DR: In this paper, the lottery tickets hypothesis is proposed to find the subnetworks that can reach test accuracy comparable to the original network in a similar number of iterations, where the winning tickets have won the initialization lottery: their connections have initial weights that make training particularly effective.
References
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