Extreme Memorization via Scale of Initialization

We construct an experimental setup in which changing the scale of initialization strongly impacts the implicit regularization induced by SGD, interpolating from good generalization performance to completely memorizing the training set while making little progress on the test set. Moreover, we find that the extent and manner in which generalization ability is affected depends on the activation and loss function used, with $\sin$ activation being the most extreme. In the case of the homogeneous ReLU activation, we show that this behavior can be attributed to the loss function. Our empirical investigation reveals that increasing the scale of initialization could cause the representations and gradients to be increasingly misaligned across examples in the same class. We further demonstrate that a similar misalignment phenomenon occurs in other scenarios affecting generalization performance, such as changes to the architecture or data distribution.