[D] Implementing a variable length input vectors for Neural Networks • r/MachineLearning

I saw a post of some time ago where somebody wanted to make use of variable length input vectors where the input would represent the pixels of multiple pictures and the resolution of the pictures would change thus the input. It seems a bit impossible as removing, changing or adding input nodes makes an impact on the network it self as weights and biases would have to be readjusted. I am doing research about this topic so if you have any insights or interesting papers to read it would be appreciated. Would the impact of removing or adding one input node on a relatively small network make a huge impact? If you keep memory of previous input nodes and place them back into the network after a few time steps would it worsen or better the network?