[quocanh]

Okay but that would only work for examples with which you already have. All interesting cases of neural networks are applying it to unseen inputs. How does your technique work with unseen inputs?

And while we interpret the result of a classification as a 1 or 0, the underlying result is a continuous probability. Even in reality, our training examples are labeled with too much confidence - some labels are vague even for humans. If it approximates a discontinuous function, then it does so by approximating a continuous function. You can read here for more information: https://www.sciencedirect.com/science/article/abs/pii/089360...

[sdenton4]

Yes, this is the point: When we train a neural network, especially on a classification problem, it has multiple avenues to solve the problem. We know they are capable of ineffectual memorization, as well as some other less ridiculous things. When we train, it's not clear what mix we're getting of 'neural hashing' vs learning abstracted features.

My point up above is that classification problems are too weak, exactly because these kinds of shortcuts are readily available. The leading edge of ML research is over-focused on ImageNet classification in particular.

[quocanh]

Ok so according to your theory, we could make this hypothesis: if we applied a neural network to an unseen example (for example, a validation dataset), then we would get accuracy that is equivalent to randomly picking a random label. Well surprise, surprise - we obviously don't get that. So there is clearly more going on than "neural hashing".

You're not answering this problem with unseen data so it's really hard for me to follow your reasoning here.