Gadam: Combining Adaptivity with Iterate Averaging Gives Greater Generalisation
We introduce Gadam, which combines Adam and iterate averaging (IA) to significantly improve generalisation performance without sacrificing adaptivity. We argue using high dimensional concentration theorems, that the noise reducing properties of IA are particularly appealing for large deep neural networks trained with small batch sizes. We contrast and compare with popular alternatives, such as the exponentially moving average (EMA), batch size increases or learning rate decreases. Furthermore, under mild conditions adaptive methods enjoy improved pre-asymptotic convergence, hence in finite time we expect this combination to be more effective than SGD + IA. We show that the combination of decoupled weight decay and IA allows for a high effective learning rate in networks with batch normalisation, which exerts additional regularisation. For language tasks (PTB) we show that Gadam is superior to finely tuned SGD, SGD with IA and Adam by a significant margin. For various image classification tasks (CIFAR-10/100, ImageNet-32) Gadam is consistently superior to finely tuned SGD and its partially adaptive variant GadamX outperforms SGD with IA.