Tag: Neural Network

6 Posts

Scaled-YOLOv4: Scaling Cross Stage Partial Network
Scaled-YOLOv4: Scaling Cross Stage Partial Network In this reading notes: We have reviewed some basic model scaling method: width, depth, resolution, compound scaling. We have computed the operation amount of residual blocks, and showed the relation with input image size (square), number of layers (linear), number of filters (square). We have presented the proposed Cross-Stage Partial (CSP) method that decreases the operations and improves the performance of basic CNN layers. PPT can be download from: https://connectpolyu-my.sharepoint.com/:p:/g/personal/18048204r_connect_polyu_hk/ET9zlHku9TFApqdl1A5NTV8BjFXPLizhCMupm6Ohcbehig?e=hhLlyc This is an embedded Microsoft Office presentation, powered by Office.
YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design
Paper Information Paper: YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design Authors: Yuxuan Cai, Hongjia Li, Geng Yuan, Wei Niu, Yanyu Li, Xulong Tang, Bin Ren, Yanzhi Wang Paper: https://arxiv.org/abs/2009.05697 Github: https://github.com/nightsnack/YOLObile Objective: Real-time object detection for mobile devices. Study notes and presentation: Download: https://connectpolyu-my.sharepoint.com/:p:/g/personal/18048204r_connect_polyu_hk/EcRbix5iqshBglmxuLurS-sBBFmbrk8chRkim1y54-yOXw?e=8Qdfmd This is an embedded Microsoft Office presentation, powered by Office.
DANet: Dual Attention Network for Scene Segmentatio
Abstract The paper introduces a position attention module and a channel attention module to capture global dependencies in the spatial and channel dimensions respectively. The proposed DANet adaptively integrates local semantic features using the self-attention mechanism. 摘要 本文引入了位置关注模块和通道关注模块,分别在空间和通道维度上捕捉全局依赖性。 所提出的DANet利用自注意力机制自适应地集成局部语义特征。 Outline Brief Review: attention mechanism, SE net DANet: Dual Attention Network Experiments: visualization and comparison Conclusion 大纲 回顾:注意机制、SENet DANet: 双重关注网络 实验:可视化和对比 结论 Download: https://connectpolyu-my.sharepoint.com/:p:/g/personal/18048204r_connect_polyu_hk/EbgphNjvYP5Psw5gdgDjInQBs761z4x8FYboKXF2arT6kw?e=haTOHI This is an embedded Microsoft Office presentation, powered by Office.
Deep Learning 2: Basic Theory of Convolutional Neural Network
Objectives Deep learning is a recently hot machine learning method. The deep learning architectures are formed by the composition of several nonlinear transformations with the goal to yield more abstract and extract useful representations/features. (i) Start with a revision of the basic principle of Neural Networks, neutron structure, examples of back-propagation, learning procedure and iterations (preferably with experimental results), and then (ii) discuss at least one type of deep learning architecture, with a way or ways to illustrate its working principle.  (iii) You can also give a summary of different deep learning architectures, and highlight their uses and significances with a good explanation if possible. (iv) you can also illustrate the whole procedure for its use for object recognition/classification. Outlines Introduction of Deep Neural Network and Convolutional Neural Network Milestones (some famous networks)Deep Convolutional Neural NetworkConclusion Presentation Slides [2021-06-25] A revised version can be download from Download_Link This is an embedded Microsoft Office presentation, powered by Office. References [1] He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. “Deep Residual Learning for Image Recognition.”…
Deep Learning 1: Basic Theory of Neural Network
Objectives Deep learning is a recently hot machine learning method. The deep learning architectures are formed by the composition of several nonlinear transformations with the goal to yield more abstract and extract useful representations/features. (i) Start with a revision of the basic principle of Neural Networks, neutron structure, examples of back-propagation, learning procedure and iterations (preferably with experimental results), and then (ii) discuss at least one type of deep learning architecture, with a way or ways to illustrate its working principle.  (iii) You can also give a summary of different deep learning architectures, and highlight their uses and significances with a good explanation if possible. (iv) you can also illustrate the whole procedure for its use for object recognition/classification. Outlines IntroductionEffect of a NeuronExamples of Binary ClassificationConclusion Presentation Slides Q&A Recommended References [1]      S. Ruder, “An overview of gradient descent optimization algorithms,” arXiv Prepr. arXiv1609.04747, 2016. [2]      L. Bottou, “Large-scale machine learning with stochastic gradient descent,” in Proceedings of COMPSTAT’2010, Springer, 2010, pp. 177–186. [3]      N. Andrew, K. Katanforoosh, and Y. B. Mourri, “Deep Learning,”…