Why neural networks tend to be trained to recognize multiple things instead of just one?

Question

I was watching this series: https://www.youtube.com/watch?v=aircAruvnKk

The series demonstrates neural networks by building a simple number recognizing network.

It got me thinking: Why neural networks try to recognize multiple labels instead of just one? In the above example, the network tries to recognize numbers from 0 to 9. What is the benefit of trying to recognize so many things simultaneously? Wouldn't it make it easier to reason about if there would be 10 different neural networks which would specialize to recognize only one number at a time?

Answer 1

In practice you never want to classify just a single digit rather than series. In such case you have to pass a patch of image to multiple network, which would make it inconvenient. If you built different accurate models, training parameters will not significantly reduced. For example sloppy written 6, in a single model the probability of being 6 and 0 would be close, not same if you consider likelihood you may get closest answer. While with different models the probability may vary in a greater scale and you may not have good generalization as you have in single model. At the end everything boils down to generalization and in my experience neural networks trained with multiple things have good generalization property that a single.

Answer 2

Your question seems to be talking about two slightly different topics:

• Pros and cons of 'one vs rest' approach in multi-class classification
• Use of Neural Networks in single-output vs multi-class classification problems

One vs Rest in Multi-Class Classification

Recognising digits is an example of multi-class classification. The approach you outline is the kind of approach summarised in the "One vs Rest" section of the Wikipedia page on multi-class classification. The page notes the following issues with this approach:

Firstly, the scale of the confidence values may differ between the binary classifiers. Second, even if the class distribution is balanced in the training set, the binary classification learners see unbalanced distributions because typically the set of negatives they see is much larger than the set of positives.

You might also like to look into another approach called One vs One ('One vs Rest' vs 'One vs One') which sets up the classification problem as a set of binary alternatives. In the digit recognition case you'd end up with a classifier for "1 or 2?", "1 or 3?", "1 or 4?" etc. This might help with the "4 vs 9" problem but it does mean an enormous amount of classifiers, that might be better represented in some kind of network. Perhaps even a network inspired by brain neurons.

Use of Neural Networks in single output vs multi-class classification

There is nothing magical about a neural network that means it has to be used for multi-class classification. Nor is there anything magical about it that makes it the only option for multi-class classification.

For example:

• Using a Neural Network for sentiment analysis outputs one single answer about how positive/negative a piece of text is.
• Digit recognition using SVM uses something that isn't a neural network for multi-class classification

Conclusions

A 10-class neural network is used to identify digits because this has turned out to be an efficient way of doing so when compared with one vs rest and one vs all approaches.

A bit off-topic, perhaps, but if you think about this in the context of T5, there does seem to be a trend of moving towards larger more multi-purpose models rather than lots of small specialised models.

Answer 3

Imagine a small kid who has no idea about the world around it. You teach the kid how to write the number "6" and that is the only thing that it knows.

Now, No matter what other number you show the kid , it's gonna always respond with "6" because that is the only thing it knows or it has learned.

You teach the kid how to write the number "9", so now it knows how to differentiate a "6" from a "9" and no matter what other number you show the kid, there is a 50 % chance of it responding with a "6" or a "9" because it knows only that much.

The purpose of a neural network is to understand underlying distribution in data that can help it in classifying different numbers. It's important to have a classifier that understands general characteristics of numbers and help us with our task. If you have 10 neural networks trained on 10 different digits, and you show each of these networks the number "10", each network will output the number on which it was trained because that is all it knows(similar to the naive kid above).

I hope this answers your question!