Part 4/10:
The Information Disparity in Learning Signals
One prevailing idea presented in the discourse is that there is a significant disparity in the amount of information conveyed by supervised signals compared to the raw data available for unsupervised learning. For example, discussions around models trained on ImageNet—where the input images vastly outnumber the labeled outputs—underscore the notion that the richness of data vastly outweighs the useful information contained in labels (targets). Supervised tasks often require a model to learn to predict a target from a myriad of inputs, making it easier to overfit due to the low information content of the targets.