Holistic Filter Pruning for Efficient Deep Neural Networks

• URL: http://arxiv.org/abs/2009.08169v1
• Date: Thu, 17 Sep 2020 09:23:36 GMT
• Title: Holistic Filter Pruning for Efficient Deep Neural Networks
• Authors: Lukas Enderich and Fabian Timm and Wolfram Burgard
• Abstract summary: "Holistic Filter Pruning" (HFP) is a novel approach for common DNN training that is easy to implement and enables to specify accurate pruning rates. In various experiments, we give insights into the training and achieve state-of-the-art performance on CIFAR-10 and ImageNet.
• Score: 25.328005340524825
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep neural networks (DNNs) are usually over-parameterized to increase the likelihood of getting adequate initial weights by random initialization. Consequently, trained DNNs have many redundancies which can be pruned from the model to reduce complexity and improve the ability to generalize. Structural sparsity, as achieved by filter pruning, directly reduces the tensor sizes of weights and activations and is thus particularly effective for reducing complexity. We propose "Holistic Filter Pruning" (HFP), a novel approach for common DNN training that is easy to implement and enables to specify accurate pruning rates for the number of both parameters and multiplications. After each forward pass, the current model complexity is calculated and compared to the desired target size. By gradient descent, a global solution can be found that allocates the pruning budget over the individual layers such that the desired target size is fulfilled. In various experiments, we give insights into the training and achieve state-of-the-art performance on CIFAR-10 and ImageNet (HFP prunes 60% of the multiplications of ResNet-50 on ImageNet with no significant loss in the accuracy). We believe our simple and powerful pruning approach to constitute a valuable contribution for users of DNNs in low-cost applications.

Related papers

• Auto-Train-Once: Controller Network Guided Automatic Network Pruning from Scratch [72.26822499434446]
  Auto-Train-Once (ATO) is an innovative network pruning algorithm designed to automatically reduce the computational and storage costs of DNNs. We provide a comprehensive convergence analysis as well as extensive experiments, and the results show that our approach achieves state-of-the-art performance across various model architectures.
  arXiv  Detail & Related papers  (2024-03-21T02:33:37Z)
• Iterative Soft Shrinkage Learning for Efficient Image Super-Resolution [91.3781512926942]
  Image super-resolution (SR) has witnessed extensive neural network designs from CNN to transformer architectures. This work investigates the potential of network pruning for super-resolution iteration to take advantage of off-the-shelf network designs and reduce the underlying computational overhead. We propose a novel Iterative Soft Shrinkage-Percentage (ISS-P) method by optimizing the sparse structure of a randomly network at each and tweaking unimportant weights with a small amount proportional to the magnitude scale on-the-fly.
  arXiv  Detail & Related papers  (2023-03-16T21:06:13Z)
• Effective Invertible Arbitrary Image Rescaling [77.46732646918936]
  Invertible Neural Networks (INN) are able to increase upscaling accuracy significantly by optimizing the downscaling and upscaling cycle jointly. A simple and effective invertible arbitrary rescaling network (IARN) is proposed to achieve arbitrary image rescaling by training only one model in this work. It is shown to achieve a state-of-the-art (SOTA) performance in bidirectional arbitrary rescaling without compromising perceptual quality in LR outputs.
  arXiv  Detail & Related papers  (2022-09-26T22:22:30Z)
• FxP-QNet: A Post-Training Quantizer for the Design of Mixed Low-Precision DNNs with Dynamic Fixed-Point Representation [2.4149105714758545]
  We propose a novel framework referred to as the Fixed-Point Quantizer of deep neural Networks (FxP-QNet) FxP-QNet adapts the quantization level for each data-structure of each layer based on the trade-off between the network accuracy and the low-precision requirements. Results show that FxP-QNet-quantized AlexNet, VGG-16, and ResNet-18 reduce the overall memory requirements of their full-precision counterparts by 7.16x, 10.36x, and 6.44x with less than 0.95%, 0.95%, and 1.99%
  arXiv  Detail & Related papers  (2022-03-22T23:01:43Z)
• Effective Model Sparsification by Scheduled Grow-and-Prune Methods [73.03533268740605]
  We propose a novel scheduled grow-and-prune (GaP) methodology without pre-training the dense models. Experiments have shown that such models can match or beat the quality of highly optimized dense models at 80% sparsity on a variety of tasks.
  arXiv  Detail & Related papers  (2021-06-18T01:03:13Z)
• A Framework For Pruning Deep Neural Networks Using Energy-Based Models [45.4796383952516]
  A typical deep neural network (DNN) has a large number of trainable parameters. We propose a framework for pruning DNNs based on a population-based global optimization method. Experiments on ResNets, AlexNet, and SqueezeNet show a pruning rate of more than $50%$ of the trainable parameters.
  arXiv  Detail & Related papers  (2021-02-25T21:44:19Z)
• Filter Pre-Pruning for Improved Fine-tuning of Quantized Deep Neural Networks [0.0]
  We propose a new pruning method called Pruning for Quantization (PfQ) which removes the filters that disturb the fine-tuning of the DNN. Experiments using well-known models and datasets confirmed that the proposed method achieves higher performance with a similar model size.
  arXiv  Detail & Related papers  (2020-11-13T04:12:54Z)
• Learning Sparse Filters in Deep Convolutional Neural Networks with a l1/l2 Pseudo-Norm [5.3791844634527495]
  Deep neural networks (DNNs) have proven to be efficient for numerous tasks, but come at a high memory and computation cost. Recent research has shown that their structure can be more compact without compromising their performance. We present a sparsity-inducing regularization term based on the ratio l1/l2 pseudo-norm defined on the filter coefficients.
  arXiv  Detail & Related papers  (2020-07-20T11:56:12Z)
• Large-Scale Gradient-Free Deep Learning with Recursive Local Representation Alignment [84.57874289554839]
  Training deep neural networks on large-scale datasets requires significant hardware resources. Backpropagation, the workhorse for training these networks, is an inherently sequential process that is difficult to parallelize. We propose a neuro-biologically-plausible alternative to backprop that can be used to train deep networks.
  arXiv  Detail & Related papers  (2020-02-10T16:20:02Z)
• Widening and Squeezing: Towards Accurate and Efficient QNNs [125.172220129257]
  Quantization neural networks (QNNs) are very attractive to the industry because their extremely cheap calculation and storage overhead, but their performance is still worse than that of networks with full-precision parameters. Most of existing methods aim to enhance performance of QNNs especially binary neural networks by exploiting more effective training techniques. We address this problem by projecting features in original full-precision networks to high-dimensional quantization features.
  arXiv  Detail & Related papers  (2020-02-03T04:11:13Z)

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.