Adding Gradient Noise Improves Learning for Very Deep Networks

How a Little Noise Helps Deep Neural Networks Learn Faster

Scientists found a tiny trick that makes training very deep AI models easier, and you can try it without fancy tools.
By adding a bit of gradient noise during learning, networks learn to find better solutions and usually generalize more, so they don't just memorize data.
The idea works on many models, from simple feedforward nets to big memory systems, and it often leads to better accuracy and lower training loss.
It's low-cost, easy to add, and sometimes lets a plain 20-layer network train with basic methods, even if initialization was bad.
In many tests researchers saw clear wins, like big error drops on hard question-answer tasks and more successful runs for hard problems.
This approach is about making optimization simpler, not changing the whole model.
Try it if you train deep models, it might boost results when other tricks fail.
The method is small, practical, and surprisingly robust, and could help improve many modern AI systems without extra complexity or heavy compute.

Read article comprehensive review in Paperium.net:
Adding Gradient Noise Improves Learning for Very Deep Networks

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