AdaPruner: Adaptive Channel Pruning and Effective Weights Inheritance
- URL: http://arxiv.org/abs/2109.06397v1
- Date: Tue, 14 Sep 2021 01:52:05 GMT
- Title: AdaPruner: Adaptive Channel Pruning and Effective Weights Inheritance
- Authors: Xiangcheng Liu, Jian Cao, Hongyi Yao, Wenyu Sun, Yuan Zhang
- Abstract summary: We propose a pruning framework that adaptively determines the number of each layer's channels as well as the wights inheritance criteria for sub-network.
AdaPruner allows to obtain pruned network quickly, accurately and efficiently.
On ImageNet, we reduce 32.8% FLOPs of MobileNetV2 with only 0.62% decrease for top-1 accuracy, which exceeds all previous state-of-the-art channel pruning methods.
- Score: 9.3421559369389
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Channel pruning is one of the major compression approaches for deep neural
networks. While previous pruning methods have mostly focused on identifying
unimportant channels, channel pruning is considered as a special case of neural
architecture search in recent years. However, existing methods are either
complicated or prone to sub-optimal pruning. In this paper, we propose a
pruning framework that adaptively determines the number of each layer's
channels as well as the wights inheritance criteria for sub-network. Firstly,
evaluate the importance of each block in the network based on the mean of the
scaling parameters of the BN layers. Secondly, use the bisection method to
quickly find the compact sub-network satisfying the budget. Finally, adaptively
and efficiently choose the weight inheritance criterion that fits the current
architecture and fine-tune the pruned network to recover performance. AdaPruner
allows to obtain pruned network quickly, accurately and efficiently, taking
into account both the structure and initialization weights. We prune the
currently popular CNN models (VGG, ResNet, MobileNetV2) on different image
classification datasets, and the experimental results demonstrate the
effectiveness of our proposed method. On ImageNet, we reduce 32.8% FLOPs of
MobileNetV2 with only 0.62% decrease for top-1 accuracy, which exceeds all
previous state-of-the-art channel pruning methods. The code will be released.
Related papers
- Network Pruning Spaces [12.692532576302426]
Network pruning techniques, including weight pruning and filter pruning, reveal that most state-of-the-art neural networks can be accelerated without a significant performance drop.
This work focuses on filter pruning which enables accelerated inference with any off-the-shelf deep learning library and hardware.
arXiv Detail & Related papers (2023-04-19T06:52:05Z) - Pruning Very Deep Neural Network Channels for Efficient Inference [6.497816402045099]
Given a trained CNN model, we propose an iterative two-step algorithm to effectively prune each layer.
VGG-16 achieves the state-of-the-art results by 5x speed-up along with only 0.3% increase of error.
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.
arXiv Detail & Related papers (2022-11-14T06:48:33Z) - Revisiting Random Channel Pruning for Neural Network Compression [159.99002793644163]
Channel (or 3D filter) pruning serves as an effective way to accelerate the inference of neural networks.
In this paper, we try to determine the channel configuration of the pruned models by random search.
We show that this simple strategy works quite well compared with other channel pruning methods.
arXiv Detail & Related papers (2022-05-11T17:59:04Z) - Searching for Network Width with Bilaterally Coupled Network [75.43658047510334]
We introduce a new supernet called Bilaterally Coupled Network (BCNet) to address this issue.
In BCNet, each channel is fairly trained and responsible for the same amount of network widths, thus each network width can be evaluated more accurately.
We propose the first open-source width benchmark on macro structures named Channel-Bench-Macro for the better comparison of width search algorithms.
arXiv Detail & Related papers (2022-03-25T15:32:46Z) - Group Fisher Pruning for Practical Network Compression [58.25776612812883]
We present a general channel pruning approach that can be applied to various complicated structures.
We derive a unified metric based on Fisher information to evaluate the importance of a single channel and coupled channels.
Our method can be used to prune any structures including those with coupled channels.
arXiv Detail & Related papers (2021-08-02T08:21:44Z) - BCNet: Searching for Network Width with Bilaterally Coupled Network [56.14248440683152]
We introduce a new supernet called Bilaterally Coupled Network (BCNet) to address this issue.
In BCNet, each channel is fairly trained and responsible for the same amount of network widths, thus each network width can be evaluated more accurately.
Our method achieves state-of-the-art or competing performance over other baseline methods.
arXiv Detail & Related papers (2021-05-21T18:54:03Z) - Network Pruning via Resource Reallocation [75.85066435085595]
We propose a simple yet effective channel pruning technique, termed network Pruning via rEsource rEalLocation (PEEL)
PEEL first constructs a predefined backbone and then conducts resource reallocation on it to shift parameters from less informative layers to more important layers in one round.
Experimental results show that structures uncovered by PEEL exhibit competitive performance with state-of-the-art pruning algorithms under various pruning settings.
arXiv Detail & Related papers (2021-03-02T16:28:10Z) - UCP: Uniform Channel Pruning for Deep Convolutional Neural Networks
Compression and Acceleration [24.42067007684169]
We propose a novel uniform channel pruning (UCP) method to prune deep CNN.
The unimportant channels, including convolutional kernels related to them, are pruned directly.
We verify our method on CIFAR-10, CIFAR-100 and ILSVRC-2012 for image classification.
arXiv Detail & Related papers (2020-10-03T01:51:06Z) - PruneNet: Channel Pruning via Global Importance [22.463154358632472]
We propose a simple-yet-effective method for pruning channels based on a computationally light-weight yet effective data driven optimization step.
With non-uniform pruning across the layers on ResNet-$50$, we are able to match the FLOP reduction of state-of-the-art channel pruning results.
arXiv Detail & Related papers (2020-05-22T17:09:56Z) - Network Adjustment: Channel Search Guided by FLOPs Utilization Ratio [101.84651388520584]
This paper presents a new framework named network adjustment, which considers network accuracy as a function of FLOPs.
Experiments on standard image classification datasets and a wide range of base networks demonstrate the effectiveness of our approach.
arXiv Detail & Related papers (2020-04-06T15:51:00Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.