Deep Network Pruning: A Comparative Study on CNNs in Face Recognition

• URL: http://arxiv.org/abs/2405.18302v1
• Date: Tue, 28 May 2024 15:57:58 GMT
• Title: Deep Network Pruning: A Comparative Study on CNNs in Face Recognition
• Authors: Fernando Alonso-Fernandez, Kevin Hernandez-Diaz, Jose Maria Buades Rubio, Prayag Tiwari, Josef Bigun,
• Abstract summary: We study methods for deep network compression applied to face recognition. The method is tested on three networks based on the small SqueezeNet (1.24M parameters) and the popular MobileNetv2 (3.5M) and ResNet50 (23.5M) We observe that a substantial percentage of filters can be removed with minimal performance loss.
• Score: 47.114282145442616
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The widespread use of mobile devices for all kind of transactions makes necessary reliable and real-time identity authentication, leading to the adoption of face recognition (FR) via the cameras embedded in such devices. Progress of deep Convolutional Neural Networks (CNNs) has provided substantial advances in FR. Nonetheless, the size of state-of-the-art architectures is unsuitable for mobile deployment, since they often encompass hundreds of megabytes and millions of parameters. We address this by studying methods for deep network compression applied to FR. In particular, we apply network pruning based on Taylor scores, where less important filters are removed iteratively. The method is tested on three networks based on the small SqueezeNet (1.24M parameters) and the popular MobileNetv2 (3.5M) and ResNet50 (23.5M) architectures. These have been selected to showcase the method on CNNs with different complexities and sizes. We observe that a substantial percentage of filters can be removed with minimal performance loss. Also, filters with the highest amount of output channels tend to be removed first, suggesting that high-dimensional spaces within popular CNNs are over-dimensionated.

Related papers

• OA-CNNs: Omni-Adaptive Sparse CNNs for 3D Semantic Segmentation [70.17681136234202]
  We reexamine the design distinctions and test the limits of what a sparse CNN can achieve. We propose two key components, i.e., adaptive receptive fields (spatially) and adaptive relation, to bridge the gap. This exploration led to the creation of Omni-Adaptive 3D CNNs (OA-CNNs), a family of networks that integrates a lightweight module.
  arXiv  Detail & Related papers  (2024-03-21T14:06:38Z)
• SqueezerFaceNet: Reducing a Small Face Recognition CNN Even More Via Filter Pruning [55.84746218227712]
  We develop SqueezerFaceNet, a light face recognition network which less than 1M parameters. We show that it can be further reduced (up to 40%) without an appreciable loss in performance.
  arXiv  Detail & Related papers  (2023-07-20T08:38:50Z)
• a novel attention-based network for fast salient object detection [14.246237737452105]
  In the current salient object detection network, the most popular method is using U-shape structure. We propose a new deep convolution network architecture with three contributions. Results demonstrate that the proposed method can compress the model to 1/3 of the original size nearly without losing the accuracy.
  arXiv  Detail & Related papers  (2021-12-20T12:30:20Z)
• SMOF: Squeezing More Out of Filters Yields Hardware-Friendly CNN Pruning [2.1481785388161536]
  We develop a CNN pruning framework called SMOF, which Squeezes More Out of Filters by reducing both kernel size and the number of filter channels. SMOF is friendly to standard hardware devices without any customized low-level implementations. We also support these claims via extensive experiments on various CNN structures and general-purpose processors for mobile devices.
  arXiv  Detail & Related papers  (2021-10-21T00:58:20Z)
• Greedy Network Enlarging [53.319011626986004]
  We propose a greedy network enlarging method based on the reallocation of computations. With step-by-step modifying the computations on different stages, the enlarged network will be equipped with optimal allocation and utilization of MACs. With application of our method on GhostNet, we achieve state-of-the-art 80.9% and 84.3% ImageNet top-1 accuracies.
  arXiv  Detail & Related papers  (2021-07-31T08:36:30Z)
• Container: Context Aggregation Network [83.12004501984043]
  Recent finding shows that a simple based solution without any traditional convolutional or Transformer components can produce effective visual representations. We present the model (CONText Ion NERtwok), a general-purpose building block for multi-head context aggregation. In contrast to Transformer-based methods that do not scale well to downstream tasks that rely on larger input image resolutions, our efficient network, named modellight, can be employed in object detection and instance segmentation networks.
  arXiv  Detail & Related papers  (2021-06-02T18:09:11Z)
• 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)
• Pruning CNN's with linear filter ensembles [0.0]
  We use pruning to reduce the network size and -- implicitly -- the number of floating point operations (FLOPs) We develop a novel filter importance norm that is based on the change in the empirical loss caused by the presence or removal of a component from the network architecture. We evaluate our method on a fully connected network, as well as on the ResNet architecture trained on the CIFAR-10 dataset.
  arXiv  Detail & Related papers  (2020-01-22T16:52:06Z)
• Filter Grafting for Deep Neural Networks [71.39169475500324]
  Filter grafting aims to improve the representation capability of Deep Neural Networks (DNNs) We develop an entropy-based criterion to measure the information of filters and an adaptive weighting strategy for balancing the grafted information among networks. For example, the grafted MobileNetV2 outperforms the non-grafted MobileNetV2 by about 7 percent on CIFAR-100 dataset.
  arXiv  Detail & Related papers  (2020-01-15T03:18:57Z)

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.