PAC-Bayesian framework based drop-path method for 2D discriminative convolutional network pruning

Zheng, Qinghe; Tian, Xinyu; Wu, Yulin; Su, Huake

doi:10.1007/s11045-019-00686-z

Cited by 78 publications

(26 citation statements)

References 32 publications

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“…Regularization is critical, especially in ill-posed problems with insufficient data. Regularization can be interpreted as a technique for achieving algebraic stability during the reconstruction process but is much more than a simple stabilization technique [ 57 , 58 ]. Data augmentation is a prevalent method for regularizing data.…”

Section: Discussionmentioning

confidence: 99%

Estimation of Organizational Competitiveness by a Hybrid of One-Dimensional Convolutional Neural Networks and Self-Organizing Maps Using Physiological Signals for Emotional Analysis of Employees

Alanazi

Alruwaili

Ahmad

et al. 2021

Sensors

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The theory of modern organizations considers emotional intelligence to be the metric for tools that enable organizations to create a competitive vision. It also helps corporate leaders enthusiastically adhere to the vision and energize organizational stakeholders to accomplish the vision. In this study, the one-dimensional convolutional neural network classification model is initially employed to interpret and evaluate shifts in emotion over a period by categorizing emotional states that occur at particular moments during mutual interaction using physiological signals. The self-organizing map technique is implemented to cluster overall organizational emotions to represent organizational competitiveness. The analysis of variance test results indicates no significant difference in age and body mass index for participants exhibiting different emotions. However, a significant mean difference was observed for the blood volume pulse, galvanic skin response, skin temperature, valence, and arousal values, indicating the effectiveness of the chosen physiological sensors and their measures to analyze emotions for organizational competitiveness. We achieved 99.8% classification accuracy for emotions using the proposed technique. The study precisely identifies the emotions and locates a connection between emotional intelligence and organizational competitiveness (i.e., a positive relationship with employees augments organizational competitiveness).

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Section: Discussionmentioning

confidence: 99%

Estimation of Organizational Competitiveness by a Hybrid of One-Dimensional Convolutional Neural Networks and Self-Organizing Maps Using Physiological Signals for Emotional Analysis of Employees

Alanazi

Alruwaili

Ahmad

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Antoniou et al 36 proposed to train a conditional GAN (DAGAN) to perform data augmentation. It is also worth mentioning that regularized deep learning [37][38][39][40][41] is an efficient and vital way to improve the generalization ability: the full stage data augmentation 37 plays the role of the implicit model ensemble without introducing additional model training costs; PReLU 38 is a new activation function to improve the classification performance with a fast convergence rate; LLb-SGD 40 is a simple gradient-based optimization method with computational efficiency; two-stage training method 39 could regularize the feature boundaries of deep networks from the point of view of data punishment so as to improve the generalization ability of the networks; drop-path 41 could reduce model parameters of deep networks and accelerate the network inference. On the other hand, MetaGAN 42 is a simplistic and versatile framework for improving the performance of few-shot learning models, based on the idea that generators generate fake samples that help the classifier to understand more explicit decision boundaries between different categories from several samples.…”

Section: Related Workmentioning

confidence: 99%

Fighting fire with fire: A spatial–frequency ensemble relation network with generative adversarial learning for adversarial image classification

et al. 2021

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Adversarial images generated by generative adversarial networks are not close to any existing benign images, and contain nonrobust features that have been identified as critical to the robustness of a machine learning model. Since adversarial images have an underlying distribution that differs from normal images, these kinds of images can offer valuable features for training a robust model. To deal with these special features, we focus on a novel machine learning task of adversarial images classification, where adversarial images can be used to investigate the problem of classifying adversarial images themselves. In the setting of this novel task, adversarial images are the ONLY kind of data used in training and testing, rather than not just a set of testing images as usual. To this end, we propose a novel spatial–frequency ensemble relation network with generative adversarial learning. First, we present a spatial–frequency ensemble representation learning to extract the feature of training images. Second, we design a meta‐learning‐based relation model to gain the relationship between images. Third, to achieve a robust model, we utilize generative adversarial learning and transform the relationship into a Jacobian matrix. Finally, we design a discriminator model that determines whether an adversarial image is from the matching category or not. Experimental results demonstrate that our approach achieves significantly higher performance compared with other state‐of‐the‐arts.

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“…This helps in implementing DNN models in a simple way with low computation complexity, further it enables to achieve a gradient‐based optimization of DNN objective functions. Moreover, aiming at accelerating the learning process, a pruning scheme is introduced in Reference [38], in which the model parameters of 2D deep CNN are reduced. By Zheng et al, 39 the performance of CNN models is improved using full‐stage information augmentation strategy.…”

Section: Related Workmentioning

confidence: 99%

An intelligent nonintrusive load monitoring scheme based on 2D phase encoding of power signals

et al. 2020

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Nonintrusive load monitoring (NILM) is the de facto technique for extracting device-level power consumption fingerprints at (almost) no cost from only aggregated mains readings. Specifically, there is no need to install an individual meter for each appliance. However, a robust NILM system should incorporate a precise appliance identification module that can effectively discriminate between various devices. In this context, this paper proposes a powerful method to extract accurate power fingerprints for electrical appliance identification. Rather than relying solely on time-domain (TD) analysis, this framework abstracts the phase encoding of the TD description of power signals using a two-dimensional (2D) representation. This allows mapping power trajectories to a novel 2D binary representation space, and then performing a histogramming process after converting binary codes to new decimal representations. This yields the final histogram of 2D phase encoding of power signals, namely, 2D-PEP. An empirical performance evaluation conducted with three realistic power consumption databases collected at distinct resolutions indicates that the proposed 2D-PEP descriptor achieves

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PAC-Bayesian framework based drop-path method for 2D discriminative convolutional network pruning

Cited by 78 publications

References 32 publications

Estimation of Organizational Competitiveness by a Hybrid of One-Dimensional Convolutional Neural Networks and Self-Organizing Maps Using Physiological Signals for Emotional Analysis of Employees

Estimation of Organizational Competitiveness by a Hybrid of One-Dimensional Convolutional Neural Networks and Self-Organizing Maps Using Physiological Signals for Emotional Analysis of Employees

Fighting fire with fire: A spatial–frequency ensemble relation network with generative adversarial learning for adversarial image classification

An intelligent nonintrusive load monitoring scheme based on 2D phase encoding of power signals

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