Proportionate gradient updates with PercentDelta
This addresses a training inefficiency for deep learning practitioners, but it is incremental as it builds on existing gradient update methods.
The paper tackled the problem of disproportionate gradient magnitudes across layers in deep neural networks, which slows convergence, by proposing PercentDelta to scale gradients proportionally to each tensor's current value, resulting in faster training and higher test accuracy on MNIST compared to other methods.
Deep Neural Networks are generally trained using iterative gradient updates. Magnitudes of gradients are affected by many factors, including choice of activation functions and initialization. More importantly, gradient magnitudes can greatly differ across layers, with some layers receiving much smaller gradients than others. causing some layers to train slower than others and therefore slowing down the overall convergence. We analytically explain this disproportionality. Then we propose to explicitly train all layers at the same speed, by scaling the gradient w.r.t. every trainable tensor to be proportional to its current value. In particular, at every batch, we want to update all trainable tensors, such that the relative change of the L1-norm of the tensors is the same, across all layers of the network, throughout training time. Experiments on MNIST show that our method appropriately scales gradients, such that the relative change in trainable tensors is approximately equal across layers. In addition, measuring the test accuracy with training time, shows that our method trains faster than other methods, giving higher test accuracy given same budget of training steps.