Priority of data in deep learning?

Submitted by Sixo60 t3_zk1rwd in deeplearning

As an Account executive at data collection & annotation startup, already have brief understanding of what data learning is. Would like to understand better who are the People on the other side, using/needing our services.

How important is data collection & annotation to you, in what way? How do you feel when your models lack data? What ways have you tried to get accurate data & which has proven to be best? Data-related bottlenecks?

Not trying to sell anything around here, just want to learn & understand the market.

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Extra_Intro_Version t1_izz5vsg wrote

There are so many kinds of data and so many kinds of data annotations.

For deep learning, the quantity required is high. The question is always “how much is enough?”

You kind of need to narrow your question.

If I was you, I’d look at your competitors and see what they claim to do. Often it’s shipping customers’ image data off to somewhere for cheap labor. And requires the customer to look at the annotations to verify they’re as expected. In my limited experience, there’s a LOT of review.

Data is everything.

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deepneuralnetwork t1_izxiq1k wrote

I feel sad when my model lacks data. That’s how I feel.

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Sixo60 OP t1_izy33tr wrote

& what's the company/team culture around data shortage? "Work with what you've got" or?

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chengstark t1_izygwqn wrote

In academia we usually have the data already labeled, but I did one unfortunate project where the annotation is absolutely garbage (too many mistakes). Ensuring the correctness of labeling should be one of the priorities. From my limited experience you would want collaborators with domain knowledge of the data to make sure the processing is absolutely correct.

Recent developments in self supervised learning and generalized pretrained big models may lower the amount of labeled samples needed, not sure what that would affect your product, but it seems related.

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j-solorzano t1_izyca4d wrote

Without big data there would not be deep learning, just plain old machine learning.

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Sixo60 OP t1_izydki6 wrote

Sure, but do the companies neglect the importance? For example, At ur workplace what's the use & quality of data?

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