Improving Generalization Performance by Switching from Adam to SGD

Try a Simple Switch to Make AI Learn Better

Many AI models start learning quick with a method called Adam, but later they often miss out on long-term accuracy.
Researchers found if you begin with Adam then later change to SGD, models can end up performing way better, specially on tasks like image and language learning.
The trick is to watch a tiny signal during training and when it flips, you do a clean switch.
This change costs almost nothing, and doesn't need extra settings, so anyone can try it.
Tests on popular networks showed the mixed approach usually closes the gap, meaning models generalize better to new data, not just the stuff they saw before.
It’s simple, practical and often works, so if your model learns fast but then stalls, try starting with Adam and moving to SGD, you might see better generalization without fuss.
Give it a go, it's an easy tweak that could make your models more reliable in the real world.

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Improving Generalization Performance by Switching from Adam to SGD

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