Oh, sorry (I am quite new to the field in general)! By bottleneck I meant the middle layer between input and output which has only 2 nodes (and all the other layers have more than that). So basically all the useful information in the input image should be stored in this 2 nodes layer. The problem with CNN as far as I understand (I read an Uber paper too about this a while ago) is that they can identify the presence of an object in the image, but they are translationally invariant, so they can’t really locate the position of that object within the image (but I have no better tool so far, hence here I am). I do have the separately both the small image (object) and big image (background), as I am generating the overall combination of them (the position of the object on the background) for training, but I don’t want to use that information, as ideally I want to build a NN that can be used for cases where you don’t have access to that. Thank you!