本文提供了对不同卷积操作的感性理解,这其中包含有卷积层(Convolutional),池化层(Pooling)和转置卷积层(Transposed convolutional)里面的输入形状(Input shape),核形状(Kernel shape),零填充(Zero padding),滑动步长(Stride)和输出形状(Output shape)之间的关系。 另外,本文还解释了卷积层和转置卷积层之间的关系。
https://arxiv.org/abs/1603.07285 [2] Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Alec Radford, Luke Metz, Soumith Chintala https://arxiv.org/pdf/1511.06434v2.pdf [3] Fully Convolutional Networks for Semantic Segmentation Jonathan Long, Evan Shelhamer, Tr...
随着反卷积在神经网络可视化的成功应用,其被越来越多的工作所采纳,比如:肠镜分割,生成模型。其中反卷积(Deconvolution)也有很多其他的叫法,比如: Transposed Convolutional,Fractional Strided Convolution等。 这篇文章的目的如下: 1、解释卷积层和反卷积层之间的关系; 2、弄清楚反卷积层输入特征大小和输出特征大小之间的...
[1] Zeiler M D, Krishnan D, Taylor G W, et al. Deconvolutional networks[C]. Computer Vision and Pattern Recognition, 2010. [2] Zeiler M D, Fergus R. Visualizing and Understanding Convolutional Networks[C]. European Conference on Computer Vision, 2013....
因此,结合上面所得到的结论,我们可以得出Fractionally Strided Convolution的输入输出关系为: o′=s(i′−1)+k−2po′=s(i′−1)+k−2p 参考 conv_arithmetic Is the deconvolution layer the same as a convolutional layer?
[1] Zeiler M D, Krishnan D, Taylor G W, et al. Deconvolutional networks[C]. Computer Vision and Pattern Recognition, 2010. [2] Zeiler M D, Fergus R. Visualizing and Understanding Convolutional Networks[C]. European Conference on Computer Vision, 2013....
图像文章阅读2-ZFNet-Visualizing and Understanding Convolutional Networks 转操作产生不变性,除非物体具有很强的对称性。图3 3,遮挡敏感性 文章解释了卷积神经网络达到这么好的效果是使用了什么信息实现的分类?是图像中的具体位置的像素值,还是图像中的上下文。文章回答了这个问题...(ReLU函数) 3.池化运算 4. 归一...
[3] Fully Convolutional Networks for Semantic Segmentation Jonathan Long, Evan Shelhamer, Trevor Darrell https://people.eecs.berkeley.edu/~jonlong/long_shelhamer_fcn.pdf [4] Deconvolution and Checkerboard Artifacts Augustus Odena, Vincent Dumoulin, Chris Olah ...
轻松理解转置卷积（transposedconvolution）或反卷积（deconvolu。。。本译⽂很⼤程度上保留了原⽂原貌，并添加了细节便于理解(各种指代)。在CNN中,转置卷积是⼀种上采样(up-sampling)的常见⽅法.如果你不清楚转置卷积是怎么操作 的,那么就来读读这篇⽂章吧.本⽂的notebook代码在Github.上采样的需要 在...
[3]Fully Convolutional Networks for Semantic Segmentation Jonathan Long, Evan Shelhamer, Trevor Darrell https://people.eecs.berkeley.edu/~jonlong/long_shelhamer_fcn.pdf [4]Deconvolution and Checkerboard Artifacts Augustus Odena, Vincent Dumoulin, Chris Olah ...