Shake-Shake: A simple trick that helps AI stop memorizing photos
This is about a tiny change that can make big difference when training image models.
Many networks have a couple of paths that work in parallel, and Shake-Shake just mixes those paths in a random way while training, so the model can't just memorize every picture.
The result is models that generalize better and make fewer mistakes on new images.
Tests on common image sets showed much better accuracy, beating some earlier single-run results, and the idea still helps even on simpler network designs that don't use extra fixes.
It's easy to add to existing setups, and people who tried it saw less overfitting without losing speed.
The team shared the code, so anyone can try it on their own problems — you can find the code online and plug it in.
This trick is small, strange maybe, but real: random mixes during training push networks to learn useful patterns not just copy training photos, and that opens new uses for many projects.
Read article comprehensive review in Paperium.net:
Shake-Shake regularization
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