I’m reading through various Deep Learning research papers and I notice that almost everyone uses the Adam optimizer instead of standard Stochastic Gradient Descent (SGD). I understand both are used to minimize the loss function, but what makes Adam so much more popular for training complex neural networks? Are there cases where SGD is still preferred?
3 answers
Adam is basically "auto-pilot" for learning rates. It’s less work for the engineer, which is why it's the go-to for most projects.
The primary advantage of Adam (Adaptive Moment Estimation) is that it maintains a separate learning rate for each parameter and adjusts them as training progresses. It combines the benefits of "Momentum" (which helps accelerate through flat regions) and "RMSProp" (which deals with rapidly changing gradients). This makes it very robust to hyperparameter settings; you can often get great results with the default learning rate of 0.001. Standard SGD, on the other hand, requires much more careful tuning of the learning rate and a decay schedule to reach the same level of performance, making Adam the "easy button" for most.
Did you know that some researchers still prefer SGD with Momentum for final production models because it is thought to generalize better than Adam?
I’ve actually seen that in several Computer Vision papers, Christopher! While Adam converges faster during the early phases, it can sometimes get stuck in "sharp" local minima. SGD often finds "flatter" minima which tend to generalize better on unseen test data. Many top practitioners actually start with Adam to get a quick working model, and then switch to SGD for the final "fine-tuning" stages to squeeze out that last 1-2% of accuracy.
So true, Sarah. When you're iterating on architectures, you don't want to spend three days just finding the right learning rate for a simple SGD setup.