Performance-aware Approximation of Global Channel Pruning for Multitask
CNNs
- URL: http://arxiv.org/abs/2303.11923v1
- Date: Tue, 21 Mar 2023 15:15:21 GMT
- Title: Performance-aware Approximation of Global Channel Pruning for Multitask
CNNs
- Authors: Hancheng Ye, Bo Zhang, Tao Chen, Jiayuan Fan, and Bin Wang
- Abstract summary: Global channel pruning (GCP) aims to remove a subset of channels (filters) across different layers from a deep model without hurting the performance.
We propose a Performance-Aware Global Channel Pruning (PAGCP) framework.
Experiments on several multitask datasets show that the proposed PAGCP can reduce the FLOPs and parameters by over 60% with minor performance drop.
- Score: 13.356477450355547
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Global channel pruning (GCP) aims to remove a subset of channels (filters)
across different layers from a deep model without hurting the performance.
Previous works focus on either single task model pruning or simply adapting it
to multitask scenario, and still face the following problems when handling
multitask pruning: 1) Due to the task mismatch, a well-pruned backbone for
classification task focuses on preserving filters that can extract
category-sensitive information, causing filters that may be useful for other
tasks to be pruned during the backbone pruning stage; 2) For multitask
predictions, different filters within or between layers are more closely
related and interacted than that for single task prediction, making multitask
pruning more difficult. Therefore, aiming at multitask model compression, we
propose a Performance-Aware Global Channel Pruning (PAGCP) framework. We first
theoretically present the objective for achieving superior GCP, by considering
the joint saliency of filters from intra- and inter-layers. Then a sequentially
greedy pruning strategy is proposed to optimize the objective, where a
performance-aware oracle criterion is developed to evaluate sensitivity of
filters to each task and preserve the globally most task-related filters.
Experiments on several multitask datasets show that the proposed PAGCP can
reduce the FLOPs and parameters by over 60% with minor performance drop, and
achieves 1.2x$\sim$3.3x acceleration on both cloud and mobile platforms.
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