23 papers · ranked by Valyu relevance
Huabin Diao, Yuexing Hao, Shaoyun Xu, Gongyan Li + 1 more
Convolutional neural networks (CNNs) have achieved significant breakthroughs in various domains, such as natural language processing (NLP), and computer vision. However, performance improvement is often accompanied by large model size and computation costs, which make it not suitable for resource-constrained devices.…
Chunlei Li, Huanyu Li, Zhoufeng Liu, Bicao Li + 2 more
'Pengcheng Liu'] Seed purity directly affects the quality of seed breeding and subsequent processing products. Seed sorting based on machine vision provides an effective solution to this problem. The deep learning technology, particularly convolutional neural networks (CNNs), have exhibited impressive performance in…
Nathan Isong
Convolutional Neural Networks (CNNs) are pivotal in image classification tasks due to their robust feature extraction capabilities. However, their high computational and memory requirements pose challenges for deployment in resource-constrained environments. This paper introduces a methodology to construct lightweight…
Ching-Chen Wang, Ching-Te Chiu, Jheng-Yi Chang
Embedding Convolutional Neural Network (CNN) into edge devices for inference is a very challenging task because such lightweight hardware is not born to handle this heavyweight software, which is the common overhead from the modern state-of-the-art CNN models. In this paper, targeting at reducing the overhead with…
Chengjie Huang, Xinjuan Sun, Yuxuan Zhang, Jiawei Xiang
The South-to-North Water Diversion Project in China is an extensive inter-basin water transfer project, for which ensuring the safe operation and maintenance of infrastructure poses a fundamental challenge. In this context, structural health monitoring is crucial for the safe and efficient operation of hydraulic…
Debesh Jha, Anis Yazidi, Michael A. Riegler, Dag Johansen + 2 more
'Håvard D. Johansen' 'Pål Halvorsen'] Abstract. Deep Neural Networks (DNNs) have become the de-facto standard in computer vision, as well as in many other pattern recognition tasks. A key drawback of DNNs is that the training phase can be very computationally expensive. Organizations or individuals that cannot afford…
Liquan Zhao, Leilei Wang, Yanfei Jia, Ying Cui + 1 more
To improve accuracy of the MobileNet network, a new lightweight deep neural network is designed based on the MobileNetV2 network. Firstly, it modifies the network depth of MobileNetV2 to balance the image resolution, network width and depth to keep the gradient stable, which reduces the generation of gradient vanishing…
Zhicheng Cai, Qiu Shen
Designing light-weight CNN models with little parameters and Flops is a prominent research concern. However, three significant issues persist in the current light-weight CNNs: i) the lack of architectural consistency leads to redundancy and hindered capacity comparison, as well as the ambiguity in causation between…
Adam Germain, Alex Sabol, Anjani Chavali, Giles Fitzwilliams + 6 more
'Alexa Cooper' 'Sandra Khuon' 'Bailey Green' 'Calvin Kong' 'John Minna' 'Young-Tae Kim'] Background Identification of lung cancer subtypes is critical for successful treatment in patients, especially those in advanced stages. Many advanced and personal treatments require knowledge of specific mutations, as well as up-…
Andrea Mattia Garavagno, Daniele Leonardis, Antonio Frisoli
- Hardware-aware neural architecture search algorithm for task-specific convolutional neural networks - A novel low-cost derivative-free search strategy inspired by Occam's razor - State-of-the-art results on the Visual Wake Word dataset in just 3.1 GPU hours - Able to be executed on free subscription online GPU…
Amit Kumar Manjhvar, Rajendra Parmula
Potato leaf diseases pose a serious challenge to global food security, often leading to considerable yield losses if not detected promptly. The growing maturity of deep learning has enabled automated, high-precision plant disease recognition, even on devices with limited computational resources. In this study, several…
Yingwei Li, Xiaojie Jin, Jieru Mei, Xiaochen Lian + 6 more
'Cihang Xie' 'Qihang Yu' 'Yuyin Zhou' 'Song Bai' 'Alan Yuille'] Non-Local (NL) blocks have been widely studied in various vision tasks. However, it has been rarely explored to embed the NL blocks in mobile neural networks, mainly due to the following challenges: 1) NL blocks generally have heavy computation cost which…
Fei Yan, Zhuangzhuang Zhang, Yinping Liu, Jia Liu + 1 more
As CNNs are widely used in fields such as image classification and target detection, the total number of parameters and computation of the models is gradually increasing. In addition, the requirements on hardware resources and power consumption for deploying CNNs are becoming higher and higher, leading to CNN models…
Hou-I Liu, Marco Antonio Gutiérrez Galindo, Hongxia Xie, Lai-Kuan Wong + 3 more
Survey Authors: ['Hou-I Liu' 'Marco Antonio Gutiérrez Galindo' 'Hongxia Xie' 'Lai-Kuan Wong' 'Hong-Han Shuai' 'Yung-Yui Li' 'Wen-Huang Cheng'] Over the past decade, the dominance of deep learning has prevailed across various domains of artificial intelligence, including natural language processing, computer vision, and…
Zachary Humphreys, Xenophon Evangelopoulos, Stavros Gerolymatos, Edward O. Pyzer-Knapp + 1 more
Graph neural networks have recently met huge success in various inference tasks including materials property prediction amongst many others. Nevertheless, having an inherently locally-based representation capacity as they do, global representation of materials' structures can only only be achieved by expanding the…
Yimeng Zhang, Tai Sing Lee, Ming Li, Fang Liu + 1 more
In this study, we evaluated the convolutional neural network (CNN) method for modeling V1 neurons of awake macaque monkeys in response to a large set of complex pattern stimuli. CNN models outperformed all the other baseline models, such as Gabor-based standard models for V1 cells and various variants of generalized…
Diego E. Galvez-Aranda, Tan Le Dinh, Utkarsh Vijay, Franco M. Zanotto + 1 more
The manufacturing process of Lithium-ion battery electrodes directly affects the practical properties of the cells, such as their performance, durability, and safety. While computational physics-based modeling has been proved as a useful method to produce insights on the manufacturing properties interdependencies as…
Rajani Raman, Haruo Hosoya
Recent computational studies have emphasized layer-wise quantitative similarity between convolutional neural networks (CNNs) and the primate visual ventral stream. However, whether such similarity holds for the face-selective areas, a subsystem of the higher visual cortex, is not clear. Here, we extensively investigate…
Authors not listed
This research presents a novel approach to obstacle detection during navigation using a combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The primary objective is to generate accurate image captions that describe the content of images, which is crucial for applications such…
Sanket Kadulkar, Michael Howard, Thomas Truskett, Venkat Ganesan
We develop a convolutional neural network (CNN) model to predict the diffusivity of cations in nanoparticle-based electrolytes, and use it to identify the characteristics of morphologies which exhibit optimal transport properties. The ground truth data is obtained from kinetic Monte Carlo (kMC) simulations of cation…
Pinaki Saha, Minh Tho Nguyen
Determination and prediction of atomic cluster structures is an important endeavor in the field of nanoclusters and thereby in materials research. To a large extent the fundamental properties of a nanocluster including its chemical, optical, magnetic, mechanical and transport properties are mainly governed by the…
Qi Yan, Zhaofei Yu, Feng Chen, Jian K. Liu
Deep convolutional neural networks (CNNs) have demonstrated impressive performance on visual object classification tasks. In addition, it is a useful model for predication of neuronal responses recorded in visual system. However, there is still no clear understanding of what CNNs learn in terms of visual neuronal…
Kandan Ramakrishnan, Iris I.A. Groen, Arnold W.M. Smeulders, H. Steven Scholte + 1 more
Convolutional neural networks (CNNs) have recently emerged as promising models of human vision based on their ability to predict hemodynamic brain responses to visual stimuli measured with functional magnetic resonance imaging (fMRI). However, the degree to which CNNs can predict temporal dynamics of visual object…