How to deal with image resizing in Deep LearningThis post continues the post published by Infosimples in 19/oct/2018: https://medium.com/infosimples/does-cnn-learns-modified-inputs-bc16ae1be498TL;DR: The best way to deal with different sized images is to downscale them to match dimensions from the smallest image available.If you read out last post, you know that CNNs are able to learn information from images even if its channels are flipped, over a cost in the model accuracy.This post studies a similar problem: suppose each color channel has a different size. Let's put an end to this doubt.Below, we have channel slices and combinations of them using different upscaling algorithms:We can also test the following architecture, able to reduce bigger channels during training with convolutions:Let's call this architecture “Multiresolution CNN”The above architecture was develop with the idea that convolutions are able reduce the channels dimensions, while extracting only the most important features.