Convolution Smooth: A Post-Training Quantization Method for Convolutional Neural Networks by Yongyuan Chen, Zhendao Wang

Subjects: “…Convolutional neural network…”
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Post-Disaster Building Damage Segmentation Using Convolutional Neural Networks by Revaldi Rahmatmulya, Agung Teguh Wibowo Almais, Mokhamad Amin Hariyadi

“…Effective and efficient actions are needed to assist in the recovery following natural disasters, one of which is aiding in the identification of building damage levels post-disaster. To address this issue, this research proposes a system capable of performing segmentation to determine the level of building damage post-natural disaster using convolutional neural network methods. …”
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ClipQ: Clipping Optimization for the Post-Training Quantization of Convolutional Neural Network by Yiming Chen, Hui Zhang, Chen Zhang, Yi Liu

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Lost-minimum post-training parameter quantization method for convolutional neural network by Fan ZHANG, Yun HUANG, Zizhuo FANG, Wei GUO

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Design of Low-Cost and Highly Energy-Efficient Convolutional Neural Networks Based on Deterministic Encoding by Tiance Tong, Qiang He, Xiaofei Nie, Yudi Zhao

“…To realize high-energy-efficiency and low-cost hardware neural networks at the near-sensor end, a hardware optimization design of convolutional neural networks based on the hybrid encoding of deterministic encoding and binary encoding is proposed. …”
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Neuroevolutionary Convolutional Neural Network Design for Low-Resolution Face Recognition by Jhon I. Pilataxi, Juan P. Perez, Claudio A. Perez, Kevin W. Bowyer

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A Cascade of Encoder–Decoder with Atrous Convolution and Ensemble Deep Convolutional Neural Networks for Tuberculosis Detection by Noppadol Maneerat, Athasart Narkthewan, Kazuhiko Hamamoto

“…Using the cropped lung images, we trained several pre-trained Deep Convolutional Neural Networks (DCNNs) on the images with hyperparameters optimized by a Bayesian algorithm. …”
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Lifetime prediction of epoxy coating using convolutional neural networks and post processing image recognition methods by Fandi Meng, Yufan Chen, Jianning Chi, Huan Wang, Fuhui Wang, Li Liu

“…Initially, a targeted image recognition approach containing convolutional neural network (CNN) and post-processing was constructed for the crack area detection. …”
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Dense skip-attention for convolutional networks by Wenjie Liu, Guoqing Wu, Han Wang, Fuji Ren

“…To overcome this limitation, we propose a dense skip-attention method for convolutional networks - a simple but effective approach to boost performance. …”
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Lightweight Convolutional Network for Bearing Fault Diagnosis by LIU Hui, LI Yang, HOU Yimin

“…In the field of bearing fault diagnosis, many convolutional models with excellent performance face challenges in industrial applications due to deployment cost constraints. …”
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Multithreaded Convolution Implementation Based on Block Methods by A. V. Sharamet

“…The analysis showed that one of the possible ways to reduce time costs is a multithreaded implementation of convolution based on block algorithms. …”
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Region‐based fully convolutional networks with deformable convolution and attention fusion for steel surface defect detection in industrial Internet of Things by Meixia Fu, Jiansheng Wu, Qu Wang, Lei Sun, Zhangchao Ma, Chaoyi Zhang, Wanqing Guan, Wei Li, Na Chen, Danshi Wang, Jianquan Wang

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Learning EEG Representations With Weighted Convolutional Siamese Network: A Large Multi-Session Post-Stroke Rehabilitation Study by Shuailei Zhang, Kai Keng Ang, Dezhi Zheng, Qianxin Hui, Xinlei Chen, Yang Li, Ning Tang, Effie Chew, Rosary Yuting Lim, Cuntai Guan

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Research on Arc Fault Detection Based on Conditional Batch Normalization Convolutional Neural Network with Cost-Sensitive Multi-Feature Extraction by Xin Ning, Tianli Ding, Hongwei Zhu

“…Although existing studies have made progress in improving the accuracy of their detection, most methods have not proposed effective solutions that address the cost-sensitive problem of feature selection. Thus, a multi-feature method is proposed by combining time-domain, frequency-domain, energy, and spatial features, which are integrated into a CBN (conditional batch normalization) convolutional neural network for detection. …”
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DEVELOPMENT OF A METHOD TO REDUCE COMPUTATIONAL COSTS IN COMMUNICATION SYSTEMS WITH UFMC THROUGH THE USE OF NUMBER-THEORETIC TRANSFORMATIONS by M. I. Kalmykov, D. V. Yurdanov, I. A. Kalmykov, I. D. Efremenkov, E. A. Voloshin

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Pilot study: a low-cost, point-of-care microfluidic chip for smartphone-integrated colorimetric detection using convolutional neural networks by Mithun Kanchan, Pedapudi Anantha Hari Arun, Siddhant Rakesh Chutke

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Retracted: Detecting Distance between Surfaces of Large Transparent Material Based on Low-Cost TOF Sensor and Deep Convolutional Neural Network by Advances in Materials Science and Engineering

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Detection of Damage on Inner and Outer Races of Ball Bearings Using a Low-Cost Monitoring System and Deep Convolution Neural Networks by Handeul You, Dongyeon Kim, Juchan Kim, Keunu Park, Sangjin Maeng

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Convolutional neural networks and vision transformers for Plankton Classification by Loris Nanni, Alessandra Lumini, Leonardo Barcellona, Stefano Ghidoni

“…The study considers the creation of ensembles combining different Convolutional Neural Network (CNN) models and transformer architectures to understand whether different optimization algorithms can result in more robust and efficient classification across various plankton datasets. …”
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Tailoring convolutional neural networks for custom botanical data by Jamie R. Sykes, Katherine J. Denby, Daniel W. Franks

“…Methods We address this gap with informed data collection and the development of a new convolutional neural network architecture, PhytNet. Utilising a novel dataset of infrared cocoa tree images, we demonstrate PhytNet's development and compare its performance with existing architectures. …”
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