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I work in the field. Transfer learning helps get you decent/good models, but the best models remain ones trained on large amounts of data. You may be able to ge
by Mehdi2277 5y ago
I work in the field. Transfer learning helps get you decent/good models, but the best models remain ones trained on large amounts of data. You may be able to get away with good performance and not great on your task. For some areas you really care a lot about long tail performance (like self driving) that you will need massive dataset. For other areas if your goal is to be the best relative to other large companies you will need a massive dataset.
Transfer learning best use cases are for fast prototypes or for ml tasks that do not need state of the art performance.
- mikewarot 5y agoIf there are hundreds of people with 100,000 photos each, that collectively is a massive training database, with a lot more labels and diversity of subjects. By keeping the training data itself private, distributed and outsourced, you might be able to get otherwise unachievable levels of performance.
- BlueTemplar 5y agoThis isn't going to solve the data ownership issues though, since they contaminate the program trained on them (and its blackbox nature only makes it worse)... though I guess that specifically for copyright it's going to depend on the final usage of that tool ?