Convnets can do this. Geoffrey Hinton has a wonderful lecture, where he trained a digit recognizer on everything but 7's and 8's (IIRC.) He then let another con

Convnets can do this. Geoffrey Hinton has a wonderful lecture, where he trained a digit recognizer on everything but 7's and 8's (IIRC.) He then let another convnet tell it which numbers looked more or less like 7's and 8's. E.g. "that 9 looks kind of like an 8. That 1 looks kind of like a 7", etc. And then it was able to correctly recognize 7's and 8's, despite never having actually seen one. I'm simplifying somewhat, but it was super cool. I don't know why people are so focused on one-shot learning, or think that NNs can't do it. Neural networks learn features from lots of (possibly unlabelled) data. That's the whole point. Once you have those features, you can use them for all sorts of things. You can show it an image, and then measure how close other images are too it. Thereby learning from a single example.