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Evolving Deep Architecture Generation with Residual Connections for Image Classification Using Particle Swarm Optimization
2021
Sensors
Preserved Fulltext
A PSO variant is proposed which incorporates a new encoding scheme and a new search mechanism guided by non-uniformly randomly selected neighboring and global promising solutions for the search of optimal ...
Owing to the guidance of diverse non-uniformly selected neighboring promising solutions in combination with the swarm leader at fine-grained and global levels, the proposed model produces a rich assortment ...
Conflicts of Interest: The authors declare no conflict of interest. Sensors 2021, 21, 7936 ...
doi:10.3390/s21237936
pmid:34883940
fatcat:a3pl6q4iq5gkvdbrae7uaqg4yy
Dynamic Hierarchical Mimicking Towards Consistent Optimization Objectives
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2003.10739v1 fatcat:ihbv23p7xbfv3novjv3tm53ege
While the depth of modern Convolutional Neural Networks (CNNs) surpasses that of the pioneering networks with a significant margin, the traditional way of appending supervision only over the final classifier ...
However, it is still vulnerable to issues including interference to the hierarchical representation generation process and inconsistent optimization objectives, as illustrated theoretically and empirically ...
R-1 denotes Rank-1 accuracy. w/ pretrain and w/o pretrain means with and without ImageNet pretrained weights loaded respectively. ...
arXiv:2003.10739v2
fatcat:mn5e5yc45vbspnpl4axhczcuwe
Cyclic Differentiable Architecture Search
[article]
2022
arXiv
pre-print
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First, the search network generates an initial architecture for evaluation, and the weights of the evaluation network are optimized. ...
Repeating the above cycle results in joint optimization of the search and evaluation networks and thus enables the evolution of the architecture to fit the final evaluation network. ...
The weights of the new evaluation network are initialized with the parameters inheriting from previous training. ...
arXiv:2006.10724v4
fatcat:vbq6g2arfndxlmyceqoqfg22qi
RelativeNAS: Relative Neural Architecture Search via Slow-Fast Learning
[article]
2021
arXiv
pre-print
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The proposed RelativeNAS brings several unique advantages: (1) it achieves state-of-the-art performance on ImageNet with top-1 error rate of 24.88%, i.e. outperforming DARTS and AmoebaNet-B by 1.82% and ...
1.12% respectively; (2) it spends only nine hours with a single 1080Ti GPU to obtain the discovered cells, i.e. 3.75x and 7875x faster than DARTS and AmoebaNet respectively; (3) it provides that the discovered ...
A large network of 20 cells (i.e. s is set to 6) is built with the selected normal and reduction cells while the initial number of channels is set to 36. ...
arXiv:2009.06193v3
fatcat:6ymzh27cpzgv5drgsemoybmdde
Efficient Visual Recognition with Deep Neural Networks: A Survey on Recent Advances and New Directions
[article]
2021
arXiv
pre-print
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Deep neural networks (DNNs) have largely boosted their performances on many concrete tasks, with the help of large amounts of training data and new powerful computation resources. ...
In this paper, we present the review of the recent advances with our suggestions on the new possible directions towards improving the efficiency of DNN-related visual recognition approaches. ...
topology, e.g., SqueezeNet [75] uses amounts of 1 × 1 convolutions to replace 3 × 3 convolutions and reduce the counts of channels in the rest 3 × 3 convolutions. ...
arXiv:2108.13055v2
fatcat:nf3lymdbvzgl7otl7gjkk5qitq
Deep Learning for Generic Object Detection: A Survey
[article]
2019
arXiv
pre-print
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2019 pre-print arXiv:1809.02165v2 fatcat:5nczdjxh4ndinlwcoxgvudhlreOther Versions
Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images. ...
Given this period of rapid evolution, the goal of this paper is to provide a comprehensive survey of the recent achievements in this field brought about by deep learning techniques. ...
This work has been supported by the Center for Machine Vision and Signal Analysis at the University of Oulu (Finland) and the National Natural Science Foundation of China under Grant 61872379. ...
arXiv:1809.02165v4
fatcat:b7ozzcy46bek5jx7l3qomj6e3q
Shape Prediction of Nasal Bones by Digital 2D-Photogrammetry of the Nose Based on Convolution and Back-Propagation Neural Network
2022
Computational and Mathematical Methods in Medicine
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To boost performance and efficacy, it is deliberately constructed with many layers and different filter sizes, with less filters and optimizing parameters. ...
In conclusion, the proposed model performed the potential hybrid of CNN and BPNN with its application to give expected accuracy in terms of keypoint localization and nasal morphology regression. ...
Acknowledgments The authors would like to specially thank the support of Pham Ngoc Thach University of Medicine and Ho Chi Minh City University of Technology and Education in experimenting and collecting ...
doi:10.1155/2022/5938493
pmid:35069786
pmcid:PMC8767378
fatcat:7mjukzr3qfhhritqxohsyup5gu
Squeeze-and-Excitation Networks
[article]
2019
arXiv
pre-print
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In this work, we focus instead on the channel relationship and propose a novel architectural unit, which we term the "Squeeze-and-Excitation" (SE) block, that adaptively recalibrates channel-wise feature ...
The central building block of convolutional neural networks (CNNs) is the convolution operator, which enables networks to construct informative features by fusing both spatial and channel-wise information ...
The work is supported in part by NSFC Grants (61632003, 61620106003, 61672502, 61571439) , National Key R&D Program of China (2017YFB1002701), and Macao FDCT Grant (068/2015/A2). ...
arXiv:1709.01507v4
fatcat:ofry6usryze7dlcrbzztvozqhm
HASA: Hybrid Architecture Search with Aggregation Strategy for Echinococcosis Classification and Ovary Segmentation in Ultrasound Images
[article]
2022
arXiv
pre-print
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However, manual design and selection of suitable network architectures are time-consuming and require substantial effort of human experts. ...
The hybrid framework consists of a pre-trained backbone and several searched cells (i.e., network building blocks), which takes advantage of the strengths of both NAS and the expert knowledge from existing ...
Acknowledgment This study was supported by National Natural Science Foundation of China (Nos. 62171290, 61901275, and 62101343) ...
arXiv:2204.06697v2
fatcat:jsgs47nhlrcwrasebu2yfkmgy4
Deep Learning in Mobile and Wireless Networking: A Survey
[article]
2019
arXiv
pre-print
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We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking. ...
The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. ...
[323]
Indoor localization
CSI
RBM
Explores features of wireless channel data
and obtains optimal weights as fingerprints
Wang et al. ...
arXiv:1803.04311v3
fatcat:awuvyviarvbr5kd5ilqndpfsde
Deep Learning in Mobile and Wireless Networking: A Survey
2019
IEEE Communications Surveys and Tutorials
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The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. ...
We first briefly introduce essential background and state-of-theart in deep learning techniques with potential applications to networking. ...
After transfer through an additive white Gaussian noise channel, a receiver employs another MLP to decode messages and select the one with the highest probability of occurrence. ...
doi:10.1109/comst.2019.2904897
fatcat:xmmrndjbsfdetpa5ef5e3v4xda
A Survey on Graph-Based Deep Learning for Computational Histopathology
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2107.00272v1 fatcat:hrphnxpyd5goxkghksu4zqghfm
With the remarkable success of representation learning for prediction problems, we have witnessed a rapid expansion of the use of machine learning and deep learning for the analysis of digital pathology ...
The phenotypical and topological distribution of constituent histological entities play a critical role in tissue diagnosis. ...
that optimally represent the data [2] . ...
arXiv:2107.00272v2
fatcat:3eskkeref5ccniqsjgo3hqv2sa
Deep Learning for Generic Object Detection: A Survey
2019
International Journal of Computer Vision
Preserved Fulltext
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Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images. ...
Given this period of rapid evolution, the goal of this paper is to provide a comprehensive survey of the recent achievements in this field brought about by deep learning techniques. ...
This work has been supported by the Center for Machine Vision and Signal Analysis at the University of Oulu (Finland) and the National Natural Science Foundation of China under Grant 61872379. ...
doi:10.1007/s11263-019-01247-4
fatcat:isdmz4febvbthgowo33c6ifhm4
Exploring the Design Space of Deep Convolutional Neural Networks at Large Scale
[article]
2016
arXiv
pre-print
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Judiciously choosing benchmarks and metrics. 2. Rapidly training CNN models. 3. Defining and describing the CNN design space. 4. Exploring the design space of CNN architectures. ...
To our knowledge, there is no single CNN/DNN architecture that solves all problems optimally. Instead, the "right" CNN/DNN architecture varies depending on the application at hand. ...
[128] use a combination of pruning, quantization, and Huffman encoding to compress the weights of pretrained models by 35x with no reduction in accuracy. ...
arXiv:1612.06519v1
fatcat:jwo2gyfjvfh3lbkfdntctx24o4
Comparative Analysis of Different Machine Learning Classifiers for the Prediction of Chronic Diseases
[chapter]
2022
Comparative Analysis of Different Machine Learning Classifiers for the Prediction of Chronic Diseases
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This paper forms the basis of understanding the difficulty of the domain and the amount of efficiency achieved by the various methods recently. ...
Chronic Diseases are the most dangerous diseases for humans and have significant effects on human life. Chronic Diseases like heart disease & Diabetes are the main causes of death. ...
The performance of existing and proposed algorithms is analysed with regard to several metrics. ...
doi:10.13052/rp-9788770227667
fatcat:da47mjbbyzfwnbpde7rgbrlppe
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