Why small-batch training may make AI smarter and use less memory
When people train deep learning models they often feed data in chunks.
Using bigger chunks can speed things up, but tests shows tiny chunks tend to make models perform better on new data.
Small chunks keep gradient info more current, so the training stays stable and reliable, and it often leads to better accuracy.
Another big win is less memory used, meaning cheaper hardware can run strong models, that is great for teams with limited resources.
Experiments on common image tasks found that as chunk size grows it becomes harder to pick a learning rate that works, so training can fail more often.
The sweet spot in many tests was very small, around m = 2–32, not thousands.
This suggest people should rethink the rush to massive batches.
Try small batch runs, you might get faster progress and lower cost, it work surprisingly well for many problems.
Read article comprehensive review in Paperium.net:
Revisiting Small Batch Training for Deep Neural Networks
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