# Label A records B times or label A*B records

This question concerns pre-training data sourcing.

Suppose you have a human workforce of B individuals and a potentially unlimited source of data.

The task is labeling images with classes. These classes are somewhat subjective (emotions). This means one individual might label the same image with a different class than another individual.

For then using these labeled records as training data on a neural network that predicts classes on images, is it better to

1) have a number of records (A) labeled redundantly by all B individuals.

2) have every individual label A different records each, yielding A x B labeled records.

Intuition behind 1) is that the mean of subjective labeling would be somewhat objective. Thus training data would be mostly objective. In addition, probabilities (50% happy, 50% surprised) could be used as input.

Intuition behind 2) is that subjectiveness in labeling of individuals is natural and the NN is trained on that, becoming somewhat "general"/"objective" in it's predictions. Also, more data is always better.

Please excuse the use of subjective and objective in combination with Machine Learning. I know this might not be correct at all.