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【2018-AAAI】Spatial As Deep: Spatial CNN for Traffic Scene Understanding
概述 提出了SCNN语义分割网络 xff0c 将传统的深度逐层卷积推广到特征图中的逐片卷积 xff0c 在同一特征图的行和列上做信息传递 xff0c 可有效识别强先验结构的目标 此外论文还发布了一个大型的车道线检测数据集CULane Dat
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论文笔记 | Learning Deep Features for Discriminative Localization
作者 Bolei Zhou Aditya Khosla Agata Lapedriza Aude Oliva Antonio Torralba Bolei Zhou Abstract 受到NIN 的启发 xff0c 将global aver
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【数据聚类|深度聚类】Nearest Neighbor Matching for Deep Clustering(NNM)论文研读
文章目录 Abstract Intorduction Related work Deep Clustering Contrastive Representation Learning Methodology Unsupervised Rep
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Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey 论文阅读笔记
本文是论文的阅读笔记 Paper A Threat of Adversarial Attacks on Deep Learning in Computer Vision A Survey Author Naveed Akhtar cor n
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[论文解读]Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
Threat of Adversarial Attacks on Deep Learning in Computer Vision A Survey 文章目录 Threat of Adversarial Attacks on Deep Le
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Deep Learning Notes: Chapter 1 Introduction
前言 最近开始读 Deep Learning 一书 这让我有了一个边读书边写笔记的动机 xff1a 能够让人很轻松流畅的把握住这本书的脉络 xff0c 从而读懂这本书的核心内容 由于终究是英文表达更地道 xff0c 因此该笔记都是节选自书中
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【论文阅读】Learning Deep Features for Discriminative Localization
Abstract 研究了全局平均池化 global average pooling GAP xff0c 分析了它如何仅仅使用图像级标签训练就能使CNN具有出色的定位能力 localization ability 作者发现 xff0c 尽管G
Learning
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深度学习(Deep Learning)
知识关键点 1 人工智能 深度学习的发展历程 2 深度学习框架 3 神经网络训练方法 4 卷积神经网络 xff0c 卷积核 池化 通道 激活函数 5 循环神经网络 xff0c 长短时记忆 LSTM 门控循环单元 GRU 6 参数初始化方法
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论文笔记:Learning Deep Features for Discriminative Localization
一 这篇论文解决什么问题 原始问题 xff1a Weakly supervised object localization xff0c 研究发现 xff0c 图像分类任务上训练的CNN xff0c 可以直接用于物体定位 两个子问题 xff1
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[翻译]Learning Deep Features for Discriminative Localization
英文原文请点这里 摘要 在这项工作中 xff0c 我们重新审视了 Network in network 中提出的全局平均 池化层 xff08 global average pooling xff09 xff0c 并阐明了它是如何通过图片标签
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论文阅读:Learning Deep Features for Discriminative Localization(CAM)
Learning Deep Features for Discriminative Localization 文章目录 Learning Deep Features for Discriminative Localization摘要1 引言
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阅读:Learning Deep Features for Discriminative Localization
作者 xff1a Bolei Zhou Aditya Khosla Agata Lapedriza Aude Oliva Antonio Torralba 来源 xff1a CVPR2015 摘要 本文重新审视了 Network in ne
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人脸识别--SphereFace: Deep Hypersphere Embedding for Face Recognition
SphereFace Deep Hypersphere Embedding for Face Recognition CVPR2017 https github com wy1iu sphereface pytorch https gith
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【CAM】Learning Deep Features for Discriminative Localization
论文链接 github Abstract 1 Introduction CNN能保留位置信息 xff0c 但经过用于分类的全连接神经网络时会丢失位置信息 最近的NIN和GoogLeNet使用全卷积网络 避免使用全连接层 xff0c 来减少参
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A-Softmax的keras实现-《SphereFace: Deep Hypersphere Embedding for Face Recognition》
A Softmax的keras实现 参考文档 xff1a https www cnblogs com heguanyou p 7503025 html 注 xff1a 主体完成 xff0c 调试中 xff0c 先行记录 xff0c 待续 已
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Deep Watershed Transform for Instance Segmentation
Min Bai amp Raquel Urtasun UfT 1 传统的分水岭算法简介 图像处理中的分水岭算法常用来做图像区域分割 segmentation xff0c 基本的思路是计算一张energy map来表示图像 xff0c 其中物
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【文献笔记】【精读】Deep Learning-Based Communication Over the Air
文章地址 xff1a Deep Learning Based Communication Over the Air 建议在看这篇blog前先看这篇 xff1a 文献笔记 精读 An Introduction to Deep Learning
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【文献笔记】【精读】An Introduction to Deep Learning for the Physical Layer
文章地址 xff1a An Introduction to Deep Learning for the Physical Layer github xff1a py radio autoencoder xff08 第三方代码 xff0c 非
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[转载][paper]Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
文章目录 摘要 深度学习是当前人工智能崛起的核心 在计算机视觉领域 xff0c 它已经成为从自动驾驶汽车到监控和安全等各种应用的主力 虽然深度神经网络在解决复杂问题方面取得了惊人的成功 通常超出了人类的能力 xff0c 但最近的研究表明 x
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Adversarial Attacks on deep learning阅读笔记
Adversarial Attacks on deep learning阅读笔记 简单说说Adversarial attackAdversarial Attacks on Deep Learning Based Radio Signal C
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