Are deep neural networks dramatically overfitted? (2019)

Re:Are deep neural networks dramatically overfitted? (2019) by jmmcd on Monday April 05, 2021 @12:49PM (#26701382)

And there's no reason we couldn't have a deep learning system where the input data (images) included time-stamps and movement vectors, and it could be good both at easy image classification, and at choosing particular "head movements" like those you performed, to help resolve ambiguous cases.

Further food for thought: these ambiguous cases seem (do you agree?) to be very rare.

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Re:Are deep neural networks dramatically overfitted? (2019) by mjburgess on Tuesday April 06, 2021 @04:11AM (#26709345)

Ambiguity is the norm, it isn't rare. Almost all visual input, ie., light, is ambiguous. We (animals) use the history of our prior geometrical-light experiences (ie., walking around) to use environmental cues to resolve ambiguity.

That billions (, trillions) of images are needed to aproximate what we can do for a single instance, i think is a good guide to the magnitude of the problem.

Google the "amnes room" -- that "illusion" is how we are always seeing.

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Re:Are deep neural networks dramatically overfitted? (2019) by jmmcd on Thursday April 08, 2021 @07:40AM (#26737552)

> Ambiguity is the norm

Right. But as you say, usually our priors are good enough. The cases where we stop, double-take, and deliberately look from another angle are rare.

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