TLDR
A small change to how computers train deep learning models makes them learn better and more reliably, especially when using a popular method called Adam.
Summary
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1 Study Aim
The paper aims to investigate whether using L2 regularization (a method to prevent overfitting by penalizing large weights) or weight decay regularization (a method that shrinks weights during training) is better for training deep neural networks with both standard stochastic gradient descent (SGD) and adaptive gradient algorithms like Adam. The authors specifically want to find out why Adam often performs worse than SGD with momentum on image classification tasks and whether a simple change in how weight decay is applied can improve Adam's performance. The study wants to see if changing how regularization is done can help Adam work as well as other training methods.
2 Study Design
The authors compare the effects of L2 regularization and weight decay regularization in both SGD and Adam optimizers. They introduce a modified version of Adam, called AdamW, where weight decay is applied separately from the gradient update. The experiments use deep neural networks (ResNet architectures) on image classification datasets like CIFAR-10 and ImageNet32x32. They test different learning rate schedules (fixed, step-drop, cosine annealing) and analyze how the choice of regularization and learning rate affects model performance and hyperparameter tuning. Additional tests include longer training runs, normalized weight decay, and warm restarts to further improve results. The researchers tested their new method on popular image datasets using different training setups to see how well it works.
3 Findings
The research demonstrates that L2 regularization and weight decay are not the same for adaptive optimizers like Adam, even though they are equivalent for standard SGD. The authors show that decoupling weight decay from the gradient update (as in AdamW) leads to better generalization and lower test errors compared to using L2 regularization with Adam. AdamW also makes it easier to tune hyperparameters, as the best learning rate and weight decay values become more independent. The improvements are consistent across different datasets, network sizes, and training schedules. The paper recommends using decoupled weight decay (AdamW) for adaptive optimizers and suggests that this approach can close the performance gap between Adam and SGD with momentum on image tasks. The new way of applying weight decay helps models trained with Adam learn better and makes it easier to find good training settings.