Strided Convolution Instead of Max Pooling for Memory Efficiency of Convolutional Neural Networks

Ayachi, Riadh; Afif, Mouna; Said, Yahia; Atri, Mohamed

doi:10.1007/978-3-030-21005-2_23

Cited by 42 publications

(25 citation statements)

References 9 publications

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“…In consecutive layers, features are further reduced to the dimensions of 17 × 40, 17 × 20 and 17 × 10. Feature reduction with strides is an alternative to pooling layers in CNNs [35,36].…”

Section: Cnn Modelmentioning

confidence: 99%

Sensitivity Analysis of Sensors in a Hydraulic Condition Monitoring System Using CNN Models

König

Helmi

2020

Sensors

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Condition monitoring (CM) is a useful application in industry 4.0, where the machine’s health is controlled by computational intelligence methods. Data-driven models, especially from the field of deep learning, are efficient solutions for the analysis of time series sensor data due to their ability to recognize patterns in high dimensional data and to track the temporal evolution of the signal. Despite the excellent performance of deep learning models in many applications, additional requirements regarding the interpretability of machine learning models are getting relevant. In this work, we present a study on the sensitivity of sensors in a deep learning based CM system providing high-level information about the relevance of the sensors. Several convolutional neural networks (CNN) have been constructed from a multisensory dataset for the prediction of different degradation states in a hydraulic system. An attribution analysis of the input features provided insights about the contribution of each sensor in the prediction of the classifier. Relevant sensors were identified, and CNN models built on the selected sensors resulted equal in prediction quality to the original models. The information about the relevance of sensors is useful for the system’s design to decide timely on the required sensors.

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Section: Cnn Modelmentioning

confidence: 99%

Sensitivity Analysis of Sensors in a Hydraulic Condition Monitoring System Using CNN Models

König

Helmi

2020

Sensors

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“…In addition to the convolutional layers, other changes were suggested to stabilize the GAN's training. Replacing the pooling layers by strided convolution has shown better performance [134,6]. Therefore, it is proposed to use strided convolutions in both G and D.…”

Section: Deep Convolutional Gan (Dcgan)mentioning

confidence: 99%

A survey on GANs for computer vision: Recent research, analysis and taxonomy

Iglesias¹,

Talavera²,

Díaz-Álvarez³

2022

Preprint

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In the last few years, there have been several revolutions in the field of deep learning, mainly headlined by the large impact of Generative Adversarial Networks (GANs). GANs not only provide an unique architecture when defining their models, but also generate incredible results which have had a direct impact on society. Due to the significant improvements and new areas of research that GANs have brought, the community is constantly coming up with new researches that make it almost impossible to keep up with the times. Our survey aims to provide a general overview of GANs, showing the latest architectures, optimizations of the loss functions, validation metrics and application areas of the most widely recognized variants. The efficiency of the different variants of the model architecture will be evaluated, as well as showing the best application area; as a vital part of the process, the different metrics for evaluating the performance of GANs and the frequently used loss functions will be analyzed.The final objective of this survey is to provide a summary of the evolution and performance of the GANs which are having better results to guide future researchers in the field.

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“…On the other hand, in reference to downsampling, strided convolution layers were chosen over classical pooling because of the advantage it provides having trainable parameters as opposed to the fixed operation the second performs. In this manner, the network can learn to summarize the data, thus improving its accuracy (Ayachi et al, 2018).…”

Section: Architecturementioning

confidence: 99%

Deep learning based MRI contrast synthesis using full volume prediction using full volume prediction

Manjón¹,

Romero²,

Coupé³

2021

Biomed. Phys. Eng. Express

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In Magnetic Resonance Imaging (MRI), depending on the image acquisition settings, a large number of image types or contrasts can be generated showing complementary information of the same imaged subject. This multi-spectral information is highly beneficial since can improve MRI analysis tasks such as segmentation and registration, thanks to pattern ambiguity reduction. However, the acquisition of several contrasts is not always possible due to time limitations and patient comfort constraints. Contrast synthesis has emerged recently as an approximate solution to generate other image types different from those acquired originally. Most of the previously proposed methods for contrast synthesis are slice-based which result in intensity inconsistencies between neighbor slices when applied in 3D. We propose the use of a 3D convolutional neural network (CNN) capable of generating T2 and FLAIR images from a single anatomical T1 source volume. The proposed network is a 3D variant of the UNet that processes the whole volume at once breaking with the inconsistency in the resulting output volumes related to 2D slice or patch-based methods. Since working with a full volume at once has a huge memory demand we have introduced a spatial-to-depth and a reconstruction layer that allows working with the full volume but maintain the required network complexity to solve the problem. Our approach enhances the coherence in the synthesized volume while improving the accuracy thanks to the integrated three-dimensional context-awareness. Finally, the proposed method has been validated with a segmentation method, thus demonstrating its usefulness in a direct and relevant application.

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Strided Convolution Instead of Max Pooling for Memory Efficiency of Convolutional Neural Networks

Cited by 42 publications

References 9 publications

Sensitivity Analysis of Sensors in a Hydraulic Condition Monitoring System Using CNN Models

Sensitivity Analysis of Sensors in a Hydraulic Condition Monitoring System Using CNN Models

A survey on GANs for computer vision: Recent research, analysis and taxonomy

Deep learning based MRI contrast synthesis using full volume prediction using full volume prediction

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