Teaching Computers to See With Fewer Photos
Researchers found that small tricks can make a big difference when a computer learns from pictures.
By cutting, turning, and flipping photos the machine sees more variations, so it learns better even when there are only a few shots.
They also tried using creative image generators to make new pictures, which sometimes helped, and sometimes not.
The most interesting part was letting the network learn how to change images itself — called neural augmentation — that idea showed promise but had limits, and needs more tuning.
This means we can get better results without buying tons of data, and your phone photos might teach models more than you think.
Some methods work well across many sets of pictures, some fail in odd ways, so more work is needed.
The takeaway, simple and surprising: small changes to images can boost learning, and new creative methods could make teaching computers cheaper and faster, if the right mix is found.
Read article comprehensive review in Paperium.net:
The Effectiveness of Data Augmentation in Image Classification using DeepLearning
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