Salient deconvolutional networks-优快云博客

本文链接：https://blog.youkuaiyun.com/u013645510/article/details/53086774

本文探讨了DeConvNets在从非监督学习到可视化应用的发展，特别是在理解并可视化卷积神经网络（CNN）中起到的关键作用。随着研究的深入，DeConvNets也被应用于语义图像分割。通过分析相关文献，揭示了DeConvNets如何帮助生成图像并用于理解深度学习模型的内在工作原理。

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Related work
DeConvNets最初用于非监督学习【1】【2】，后被用于可视化【3】。文章【4】基于文章【5】，文章【5】是在先前hog,sift,BoVW或其他一些神经网络表达基础上。【6】学习第二个网络用于第一个网络的inverse。【7,8,9】利用cnn的特性，生成图像confuse them。本文与【10】的工作相似。
近期DeConvNets被用于语义图像分割
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