machine-learning - 关于 SegNet 架构中两个相继放置的卷积层

Question

在 SegNet ，作者提出的架构如下所示。 .

令我困惑的是，每个构建 block 中有两个彼此相连的卷积层，如图 1 和 2 所示。以这种方式放置卷积层而不是将它们聚合成的主要动机是什么？单个卷积层？

Answer 1

SegNet 使用 VGG 的 13 个卷积层。 (2+2+3+3+3)

检查this visualization和 the paper了解更多信息。

来自论文:

It is easy to see that a stack of two 3×3 conv. layers (without spatial pooling in between) has an effective receptive field of 5×5 such layers have a 7 × 7 effective receptive field. So what have we gained by using, for instance, a stack of three 3×3 conv. layers instead of a single 7×7 layer? First, we incorporate three non-linear rectification layers instead of a single one, which makes the decision function more discriminative. Second, we decrease the number of parameters: assuming that both the input and the output of a three-layer 3 × 3 convolution stack has C channels, the stack is parametrised by weights; at the same time, a single 7 × 7 conv. layer would require parameters, i.e. 81% more. This can be seen as imposing a regularisation on the 7 × 7 conv. filters, forcing them to have a decomposition through the 3 × 3 filters (with non-linearity injected in between).