Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

Parameter pruning is a promising approach for CNN compression and acceleration by eliminating redundant model parameters with tolerable performance loss.
Despite its effectiveness, existing regularization-based parameter pruning methods usually drive weights towards zero with large and constant regularization factors, which neglects the fact that the expressiveness of CNNs is fragile and needs a more gentle way of regularization for the networks to adapt during pruning.
To solve this problem, we propose a new regularization-based pruning method (named IncReg) to incrementally assign different regularization factors to different weight groups based on their relative importance, whose effectiveness is proved on popular CNNs compared with state-of-the-art methods.
Recently, deep Convolutional Neural Networks (CNNs) have made a remarkable success in computer vision tasks by leveraging large-scale networks learning from big amount of data.
However, CNNs usually lead to massive computation and storage consumption, thus hindering their deployment on mobile and embedded devices.
To solve this problem, many research works focus on compressing the scale of CNNs.
Parameter pruning is a promising approach for CNN compression and acceleration, which aims at eliminating redundant model parameters at tolerable performance loss.
To avoid hardware-unfriendly irregular sparsity, structured pruning is proposed for CNN acceleration BID0 BID22 .
In the im2col implementation BID1 BID3 of convolution, weight tensors are expanded into matrices, so there are generally two kinds of structured sparsity, i.e. row sparsity (or filter-wise sparsity) and column sparsity (or shape-wise sparsity) BID24 BID23 .There
are mainly two categories of structured pruning. One is
importance-based methods, which prune weights in groups based on some established importance criteria BID17 BID19 BID23 . The other
is regularization-based methods, which add group regularization terms to learn structured sparsity BID24 BID16 BID8 . Existing
group regularization approaches mainly focus on the regularization form (e.g. Group LASSO BID26 ) to learn structured sparsity, while ignoring the influence of regularization factor. In particular
, they tend to use a large and constant regularization factor for all weight groups in the network BID24 BID16 , which has two problems. Firstly, this
'one-size-fit-all' regularization scheme has a hidden assumption that all weights in different groups are equally important, which however does not hold true, since weights with larger magnitude tend to be more important than those with smaller magnitude. Secondly, few
works have noticed that the expressiveness of CNNs is so fragile BID25 during pruning that it cannot withstand a large penalty term from beginning, especially for large pruning ratios and compact networks (like ResNet BID7 ). AFP BID6 was
proposed to solve the first problem, while ignored the second one. In this paper
, we propose a new regularization-based method named IncReg to incrementally learn structured sparsity.
We propose a new structured pruning method based on an incremental way of regularization, which helps CNNs to transfer their expressiveness to the rest parts during pruning by increasing the regularization factors of unimportant weight groups little by little.
Our method is proved to be comparably effective on popular CNNs compared with state-of-the-art methods, especially in face of large pruning ratios and compact networks.
we propose a new regularization-based pruning method (named IncReg) to incrementally assign different regularization factors to different weight groups based on their relative importance.