How to train a network to recognize 'is' or 'is not'?

(Joe) #1

I don’t know how to concisely word my question to google it so I just have to explain it.

Let’s say I want to train a network to recognize a specific person, Robin Williams for example, how do I train the network to distinguish him from other people? Assuming the input is an image file with potentially several people.

The problem I’m having is I think if I train it just picture of Robin Williams, the network would learn the general features of a person (nose, eyes, mouth, etc.) and say that any person is Robin Williams. If I use two labels, one is_robin and another not_robin, what would my not_robin training set consist of? I don’t think the not_robin training set can just be random people because there wouldn’t be a pattern to learn.

Maybe a neural network isn’t the correct structure to use for this kind of question?

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#2

I would select my not_robin images to be the most similar as I would expect to get when deploying the model for whatever purpose it’s going to be deployed as. For instance, if you expect not_robin images to be consumed by your model that look like yearbook photos of people who are not Robin Williams, then you should try to source yearbook photos of people who are not Robin Williams.

I’m not sure why you came to the conclusion that deep learning isn’t an appropriate solution to this type of problem. It seems like the best solution to me from afar.

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(Joe) #3

For instance, if you expect not_robin images to be consumed by your model that look like yearbook photos of people who are not Robin Williams, then you should try to source yearbook photos of people who are not Robin Williams.

Isn’t there the potential of the network learning to recognize the structure of yearbook photos instead of just people who are not Robin Williams?

The trouble I’m having is with it is that Robin Williams belongs to the people set so he’s a subset of person. But if I train the model with images of other people and he belongs to that set, then how is the model to say “yes this is that particular person” or “no it’s not him” when all people have the same basic features, because they’re all in the same people set.

A similar example, if I want a network to recognize a Toyota Camry out of an image set of other cars.

Would it be appropriate to train a model on just images of Robin Williams then under an arbitrary confidence level (.8 for example ) just say it’s not him?

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#4

Still trying to understand the problem…what is your goal in the Robin Williams classifier. To be able to classify an image of any thing in the whole world as either being Robin Williams or not? To be able to classify picture of people as Robin William or not? Something else? I think uour goal should motivate your choices of not_robin photos.

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