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  4. A Survey on Deep Neural Network Compression: Challenges, Overview, and

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  1. Literature Review of Deep Network Compression

    Out of scope: We restrict our literature to papers that include a review of deep network compression approaches. Papers that focus on data compression are out of our survey's scope. Unlike model compression, data compression (i.e., text compression [ 19 ], genomic compression [ 20 ], and image compression [ 21 , 22 , 23 ]) forms a central ...

  2. Literature Review of Deep Network Compression

    This review also intends to clarify these major concepts, and highlights their characteristics, advantages, and shortcomings. Keywords: deep learning; neural networks pruning; model compression. 1 ...

  3. Literature Review of Deep Network Compression

    Literature Review of Deep Network Compression. Ali Alqahtani, Xianghua Xie, Mark W. Jones. Published in Informatics 17 November 2021. Computer Science. TLDR. This paper presents an overview of popular methods and review recent works on compressing and accelerating deep neural networks, considering not only pruning methods but also quantization ...

  4. PDF Literature Review of Deep Network Compression

    Informatics 2021, 8, 77 3 of 12 progress. The types of compression methods discussed below are intended to provide an overview of popular techniques used in the research of deep neural network ...

  5. Deep neural networks compression: A comparative survey and choice

    1. Introduction. The methodology behind deep neural networks (DNNs) dates back to more than forty years ago. However, the availability of dedicated hardware (such as GPUs or TPUs) and of huge datasets recently allowed to maximize the performance of several DNN-based predictors, setting in practice the state-of-the-art for several problems of image processing, financial forecasting, and so on.

  6. PDF A Survey on Deep Neural Network Compression: Challenges, Overview, and

    Categorization of compression techniques for deep neural network Fig. 1: Overview of different categories of compression techniques for deep neural network. fitted on mobile devices. They adopt parallelization of features or task distribution to reduce the storage and computation requirements of the DNN model. The existing literature [69]-

  7. Literature Review of Deep Network Compression

    Literature Review of Deep Network Compression ... Deep networks often possess a vast number of parameters, and their significant redundancy in parameterization has become a widely-recognized property. This presents significant challenges and restricts many deep learning applications, making the focus on reducing the complexity of models while ...

  8. PDF arXiv:2011.04868v2 [cs.LG] 11 Nov 2020

    The compression of deep neural networks (DNNs) to reduce inference cost be-comes increasingly important to meet realistic deployment requirements of various applications. There have been a significant amount of work regarding network ... Literature Review: There have been numerous efforts devoted to network compression (Buciluaˇ ...

  9. [2010.03954] A Survey on Deep Neural Network Compression: Challenges

    A Survey on Deep Neural Network Compression: Challenges, Overview, and Solutions. Rahul Mishra, Hari Prabhat Gupta, Tanima Dutta. Deep Neural Network (DNN) has gained unprecedented performance due to its automated feature extraction capability. This high order performance leads to significant incorporation of DNN models in different Internet of ...

  10. Model Compression for Deep Neural Networks: A Survey

    By analyzing and discussing areas related to model compression, this literature review intended to provide researchers with new information and research directions and to promote the further development of deep neural network model compression. ... S. HFPQ: Deep neural network compression by hardware-friendly pruning-quantization. Appl. Intell ...

  11. A Survey of Deep Neural Network Compression

    In recent years, many researchers have conducted a lot of research in the field of model compression, and proposed many compression methods. In this paper, according to the compression methods using neural network information in the different locations during the compression process, the existing methods of compressing deep neural networks could be divided into three categories: (1) weight ...

  12. Literature Review of Deep Network Compression

    We consider not only pruning methods but also quantization methods, and low-rank factorization methods. This review also intends to clarify these major concepts, and highlights their characteristics, advantages, and shortcomings. Keywords: deep learning; neural networks pruning; model compression: College: Faculty of Science and Engineering ...

  13. Model Compression Techniques in Deep Neural Networks

    Model Compression methods in literature can be classified in four major parts: Pruning, Knowledge Distillation (KD), Quantization, and other methods. Pruning is removing an unwanted structure from a trained network. KD is a mechanism to pass along the knowledge of a bigger model onto a smaller model.

  14. Efficient convolutional neural networks and network compression methods

    The main contributions of this review include the following aspects. As far as we know, there are few reviews on efficient object detection CNNs. ... reviewed the literature on object detection with deep CNN, in a comprehensive way, ... (2015a) Deep compression: compressing deep neural network with pruning, trained quantization and huffman ...

  15. A Survey on Deep Neural Network Compression: Challenges, Overview, and

    A comprehensive review of existing literature on compressing DNN model that reduces both storage and computation requirements is presented and the existing approaches are divided into five broad categories, i.e., network pruning, sparse representation, bits precision, knowledge distillation, and miscellaneous. Deep Neural Network (DNN) has gained unprecedented performance due to its automated ...

  16. Deep Neural Network Compression and Acceleration: A Review

    Neural Computing and Applications. 2022. TLDR. This paper is to present a review and full developing route of deep learning-based FDD in complex process industries and typical deep learning techniques, e.g., transfer learning, generative adversarial network, capsule network, graph neural network, are presented for process FDD. Expand.

  17. Deep Architectures for Image Compression: A Critical Review

    In the literature, ... who have a keen interest in state-of-the-art deep learning for image compression. This review paper is different from Dhawan [2], ... Most of the deep neural network approaches are optimized for a single compression bit-rate with a dedicated network. However, for real time applications, it is the need to design variable ...

  18. [2010.03954v1] A Survey on Deep Neural Network Compression: Challenges

    A Survey on Deep Neural Network Compression: Challenges, Overview, and Solutions. Deep Neural Network (DNN) has gained unprecedented performance due to its automated feature extraction capability. This high order performance leads to significant incorporation of DNN models in different Internet of Things (IoT) applications in the past decade ...

  19. Literature Review of Deep Network Compression

    My Research and Language Selection Sign into My Research Create My Research Account English; Help and support. Support Center Find answers to questions about products, access, use, setup, and administration.; Contact Us Have a question, idea, or some feedback? We want to hear from you.

  20. An Overview of Deep Neural Network Model Compression

    In recent years, with the rapid development of machine learning, deep neural networks have achieved great success in the fields of computer vision and natural language processing. However, accompany with the remarkable performance, came the huge amount of parameters, the high cost of storage and computing. It is a great challenge for the embedded devices, such as autonomous vehicles, robotics ...

  21. Deep Architectures for Image Compression : A Critical Review

    Image compression is widely required in vision-related processing, storage, and transmission over the Internet [1] [2] [3]. Deep image compression (DIC) is a learning-based approach that ...