Why DL generalizes well is still an open research problem AFAIK. I've read numerous papers that tries to argue one way or another why this works, and they are all interesting! One paper (that I found compelling, even though it didn't propose a thorough solution) showed that SGD successfully navigated around "bad" local minimas (with bad generalization) and ended up in a "good" local minima (that generalized well), and their interpretation was that due to the S in SGD, it will only find wide loss basins, and thus the conclusion was that solutions that generalize well seem to have wider basins (for some reason).

Another paper showed that networks trained on roughly the same dataset but initialized from different random initializations, had a symmetry relation in the loss landscape by a permutation of all the weights. You could always find a permutation that allowed you to then linearly interpolate between the two weight sets without climbing over a loss mountain. Also very interesting even if it wasn't directly related to generalization performance. It has potential applications in network merging I guess.