However, how do we train a network when we do not have any ground truth information available? Unsupervised learning is one answer to this. The idea here is to craft a function that computes the network's loss only based on its input and its corresponding output.
This strategy applies very well to applications such as clustering (grouping images with similar properties together) or compression (reducing the content size while preserving some properties). For clustering, the loss function could measure how similar images from one cluster are compared to images from other clusters. For compression, the loss function could measure how well preserved the important properties are in the compressed data compared to the original ones.
Unsupervised learning thus requires some expertise regarding the use cases so that we can come up with meaningful loss functions.
