SGDR: Stochastic Gradient Descent with Restarts

SGDR: Stochastic Gradient Descent with Restarts — Train Deep Nets Faster

This short idea shows how a small change can make training neural nets much better.
By adding simple restarts to the routine, the learning process gets fresh starts now and then, letting models escape stuck spots and try again.
The trick works with plain SGD and is easy to add, it barely costs extra time and can be used with many setups.
We tested on common image tasks and the models learned quicker, giving surprisingly low errors on standard benchmarks.
For people who build deep neural networks this means faster progress and often better results during the whole run.
The team reports new state-of-the-art numbers on popular datasets, and the code lives on GitHub so you can try it too.
It feels simple but the effect is strong, youll notice training that used to stall now keeps improving.
Try it if you train models, many will see boosts right away, some cases show big gains and others smaller, but its an easy upgrade to try.

Read article comprehensive review in Paperium.net:
SGDR: Stochastic Gradient Descent with Restarts

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