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第 12 屆 iThome 鐵人賽

DAY 4
1
AI & Data

30天只學U-net系列 第 4

[day-04] U-net 第一段introduction

前言

introduction的目的:讓初看這篇文章的人了解背景知識與問題。

從introduction 看 U-net

In the last two years, deep convolutional networks have outperformed the state of the art in many visual recognition tasks, e.g. [7,3]. While convolutional networks have already existed for a long time [8], their success was limited due to the size of the available training sets and the size of the considered networks. The breakthrough by Krizhevsky et al. [7] was due to supervised training of a large network with 8 layers and millions of parameters on the ImageNet dataset with 1 million training images. Since then, even larger and deeper networks have been trained [12].

首先,介紹了一個技術 "convolutional networks"。

[7] Alexnet,學習CNN一定要知道的network
[3] R-CNN,利用CNN解決物件偵測問題的第一人
[8] 古老的CNN
[12] VGGNet,用大型的network去辨識問題

convolutional networks

就是俗稱的CNN

ImageNet

一個非常大的標註資料數據集。

結語

U-net之前,已經發展了許多CNN技術,明天我們從U-net的架構,來剖析一下CNN有哪些發展吧~~

參考文獻

上古時期的CNN
Alexnet
R-CNN
VGGNet


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[day-03] U-net 摘要
下一篇
[day-05] 第二段 introduction
系列文
30天只學U-net30

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