完全卷积神经网络的反卷积/上采样层规范

Question

我想了解fully convolutional neural network model的实施情况。在论文中，作者讨论了三种模型：fcn8，fcn16和fcn 32。 。

以下是文件中fcn16的详细说明

We first divide the output stride in half by predicting
from a 16 pixel stride layer. We add a 1 × 1 convolution
layer on top of pool4 to produce additional class predictions.
We fuse this output with the predictions computed
on top of conv7 (convolutionalized fc7) at stride 32 by
adding a 2× upsampling layer and summing6 both predictions
(see Figure 3). We initialize the 2× upsampling to bilinear
interpolation, but allow the parameters to be learned
as described in Section 3.3. Finally, the stride 16 predictions
are upsampled back to the image. 

对于来自pool4的跳过层和来自原始卷积层7的预测层，没有明确规定去卷积滤波器的内核大小。另外，作者提到在conv7之上添加2 *上采样层。这是否意味着步幅= 2？同样对于这种情况，解卷积层的内核大小应该是什么？