466 research outputs found
Asymptotic Soft Filter Pruning for Deep Convolutional Neural Networks
Deeper and wider Convolutional Neural Networks (CNNs) achieve superior
performance but bring expensive computation cost. Accelerating such
over-parameterized neural network has received increased attention. A typical
pruning algorithm is a three-stage pipeline, i.e., training, pruning, and
retraining. Prevailing approaches fix the pruned filters to zero during
retraining, and thus significantly reduce the optimization space. Besides, they
directly prune a large number of filters at first, which would cause
unrecoverable information loss. To solve these problems, we propose an
Asymptotic Soft Filter Pruning (ASFP) method to accelerate the inference
procedure of the deep neural networks. First, we update the pruned filters
during the retraining stage. As a result, the optimization space of the pruned
model would not be reduced but be the same as that of the original model. In
this way, the model has enough capacity to learn from the training data.
Second, we prune the network asymptotically. We prune few filters at first and
asymptotically prune more filters during the training procedure. With
asymptotic pruning, the information of the training set would be gradually
concentrated in the remaining filters, so the subsequent training and pruning
process would be stable. Experiments show the effectiveness of our ASFP on
image classification benchmarks. Notably, on ILSVRC-2012, our ASFP reduces more
than 40% FLOPs on ResNet-50 with only 0.14% top-5 accuracy degradation, which
is higher than the soft filter pruning (SFP) by 8%.Comment: Extended Journal Version of arXiv:1808.0686
Accelerating Deep Convolutional Neural Networks via Filter Pruning
University of Technology Sydney. Faculty of Engineering and Information Technology.The superior performance of deep Convolutional Neural Networks (CNNs) usually comes from the deeper and wider architectures, which cause the prohibitively expensive computation cost. To reduce the computational cost, works on model compression and acceleration have recently emerged. Among all the directions for this goal, filter pruning has attracted attention in recent studies due to its efficacy. For a better understanding of filter pruning, this thesis explores different aspects of filter pruning, including pruning mechanism, pruning ratio, pruning criteria, and automatic pruning. First, we improve the pruning mechanism with soft filter pruning so that the mistaken pruned filters can have a chance to be recovered. Second, we consider the asymptotic pruning rate to reduce the sudden information loss in the pruning process. Then we explore the pruning criteria to better measure the importance of filters. Finally, we propose the automatic pruning method to save human labor. Our methods lead to superior convolutional neural network acceleration results
Recognition of Defective Mineral Wool Using Pruned ResNet Models
Mineral wool production is a non-linear process that makes it hard to control
the final quality. Therefore, having a non-destructive method to analyze the
product quality and recognize defective products is critical. For this purpose,
we developed a visual quality control system for mineral wool. X-ray images of
wool specimens were collected to create a training set of defective and
non-defective samples. Afterward, we developed several recognition models based
on the ResNet architecture to find the most efficient model. In order to have a
light-weight and fast inference model for real-life applicability, two
structural pruning methods are applied to the classifiers. Considering the low
quantity of the dataset, cross-validation and augmentation methods are used
during the training. As a result, we obtained a model with more than 98%
accuracy, which in comparison to the current procedure used at the company, it
can recognize 20% more defective products.Comment: 6 pages, 5 figures, 3 tables Submitted on IEEE Transactions on
Industrial Informatic
WHC: Weighted Hybrid Criterion for Filter Pruning on Convolutional Neural Networks
Filter pruning has attracted increasing attention in recent years for its
capacity in compressing and accelerating convolutional neural networks. Various
data-independent criteria, including norm-based and relationship-based ones,
were proposed to prune the most unimportant filters. However, these
state-of-the-art criteria fail to fully consider the dissimilarity of filters,
and thus might lead to performance degradation. In this paper, we first analyze
the limitation of relationship-based criteria with examples, and then introduce
a new data-independent criterion, Weighted Hybrid Criterion (WHC), to tackle
the problems of both norm-based and relationship-based criteria. By taking the
magnitude of each filter and the linear dependence between filters into
consideration, WHC can robustly recognize the most redundant filters, which can
be safely pruned without introducing severe performance degradation to
networks. Extensive pruning experiments in a simple one-shot manner demonstrate
the effectiveness of the proposed WHC. In particular, WHC can prune ResNet-50
on ImageNet with more than 42% of floating point operations reduced without any
performance loss in top-5 accuracy.Comment: Accepted by ICASSP 202
A Differentiable Framework for End-to-End Learning of Hybrid Structured Compression
Filter pruning and low-rank decomposition are two of the foundational
techniques for structured compression. Although recent efforts have explored
hybrid approaches aiming to integrate the advantages of both techniques, their
performance gains have been modest at best. In this study, we develop a
\textit{Differentiable Framework~(DF)} that can express filter selection, rank
selection, and budget constraint into a single analytical formulation. Within
the framework, we introduce DML-S for filter selection, integrating scheduling
into existing mask learning techniques. Additionally, we present DTL-S for rank
selection, utilizing a singular value thresholding operator. The framework with
DML-S and DTL-S offers a hybrid structured compression methodology that
facilitates end-to-end learning through gradient-base optimization.
Experimental results demonstrate the efficacy of DF, surpassing
state-of-the-art structured compression methods. Our work establishes a robust
and versatile avenue for advancing structured compression techniques.Comment: 11 pages, 5 figures, 6 table
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