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segmentation papers in deep learning下载
weixin_39820535
2019-09-29 12:00:27
segmentation papers in deep learning in pdf formats
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segmentation papers in deep learning下载
segmentation papers in deep learning in pdf formats 相关下载链接://download.csdn.net/download/mjy304122/10210495?utm_source=bbsseo
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segmentat
ion
papers
in
deep
l
ear
ning
segmentat
ion
papers
in
deep
l
ear
ning
in pdf format
detect
ion
papers
in
deep
l
ear
ning
detect
ion
papers
in
deep
l
ear
ning
in pdf format from 2013
deep
domain adaptat
ion
tutorial-small.pdf
Deep
domain adaptat
ion
has emerged as a new l
ear
ning
technique to address the lack of massive amounts of labeled data. Compared to convent
ion
al methods, which l
ear
n shared feature subspaces or reuse important source instances with shallow representat
ion
s,
deep
domain adaptat
ion
methods leverage
deep
networks to l
ear
n more transferable representat
ion
s by embedding domain adaptat
ion
in the pipeline of
deep
l
ear
ning
. There have been comprehensive surveys for shallow domain adaptat
ion
, but few timely reviews the emerging
deep
l
ear
ning
based methods. In this paper, we provide a comprehensive survey of
deep
domain adaptat
ion
methods for computer vis
ion
applicat
ion
s with four major contribut
ion
s. First, we present a taxonomy of different
deep
domain adaptat
ion
scenarios according to the properties of data that define how two domains are diverged. Second, we summarize
deep
domain adaptat
ion
approaches into several categories based on trai
ning
loss, and analyze and compare briefly the state-of-the-art methods under these categories. Third, we overview the computer vis
ion
applicat
ion
s that go beyond image classificat
ion
, such as face recognit
ion
, semantic
segmentat
ion
and object detect
ion
. Fourth, some potential deficiencies of current methods and several future direct
ion
s are highlighted
What are the most interesting CVPR 2014
papers
What are the most interesting CVPR 2014
papers
自己整理的一些论文,对于想看CVPR论文的朋友会有帮助
Best_AI_paper_2020:按发布日期整理的AI最新突破清单,其中包含清晰的视频说明,指向更深入的文章和代码的链接
Best_AI_paper_2020:按发布日期整理的AI最新突破清单,其中包含清晰的视频说明,指向更深入的文章和代码的链接
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