Deep Learning is Robust to Massive Label Noise

Neural networks still learn when most labels are wrong

Researchers found that modern image models can learn even if most of the training labels are bad.
You read that right — a model can reach high accuracy after seeing many wrong tags, as long as there is enough data.
On simple digit pictures and bigger photo sets the networks kept learning, even when each correct example was mixed with lots of random labels.
This means with more examples you can tolerate lots of noise, so collecting cheap, messy data becomes useful.
Training like this needs a bigger dataset, but not impossibly large, and it seems the noise just makes the model learn slower by shrinking the effective group of examples it learns from each step.
That insight could let teams build smart systems without perfect labels and still get strong results.
It's exciting because it opens ways to use huge, low-cost datasets and still keep good performance, while saving time and money for real world projects.

Read article comprehensive review in Paperium.net:
Deep Learning is Robust to Massive Label Noise

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