What is unpadded convolution?
Padding: the padding defines how the border of an image is handled. On the other hand, unpadded convolution (‘valid’ padding in Tensorflow) only perform convolution on the pixels of the input image, without adding 0 around the input boundaries. The output size is smaller than the input size.
Why is it called transposed convolution?
It is also known as upsampled convolution which is intuitive to the task it is used to perform, i.e upsample the input feature map. Finally, it is also referred to as Backward strided convolution because forward pass in a Transposed Convolution is equivalent to backward pass of a normal convolution.
Can a convolution have a stride of 0?
The stride is the sliding step. You can not have a stride of 0, this would mean not sliding at all. In the paper they use a convolution with a 2X2 stride, the step is 2 in both x and y direction, followed by non-strided convolution, stride 1, step 1.
What does stride mean in context of convolutional neural neural?
Convolution filter’s size: e.g. 11×11 with stride 4 in (Krizhevsky et al., 2012), or 7×7 with stride 2 in (Zeiler & Fergus,2013; Sermanet et al., 2014) [ 1], 3×3 with stride 1. The size of convolution is ability to hold in formation, if you use filter 11×11, and set stride 1, the is almost no different between 2 neighbor filter.
Is the stride always symmetrical in height and width?
The amount of movement between applications of the filter to the input image is referred to as the stride, and it is almost always symmetrical in height and width dimensions. The default stride or strides in two dimensions is (1,1) for the height and the width movement, performed when needed. And this default works well in most cases.
Why are padding and stride important in convolutional layer?
Although the convolutional layer is very simple, it is capable of achieving sophisticated and impressive results. Nevertheless, it can be challenging to develop an intuition for how the shape of the filters impacts the shape of the output feature map and how related configuration hyperparameters such as padding and stride should be configured.