DLS Course 4 Week 2 Exercise 1: 1x1 convolution with strides=2

[Jerome_Griffon]

You can find some answers in this discussion:

Course 4 week 2: Residual Networks - kernel size=1 stride = 2

In the residual network, for the first filter of the convolutional block we use: kernel size = (1,1) and stride = (s,s), where s is usually 2. Doesn’t that mean that we just throw away 75% of the input information? That doesn’t seem to make sense to me. What I would expect: the first and third filter to have kernel size(1,1) and stride (1,1), and the second filter to have kernel size (f,f) and stride(s,s). So that we don’t throw away information. Could someone help me to understand?