# Unsupervised Learning

The paper argues that unsupervised learning, including self-supervised learning, has no single goal or single maximized objective function, and proposes four distinct goals that are partly orthogonal or even conflicting: estimating the distribution, generating new data points, extracting features for downstream tasks, and understanding the data; for each goal it gives a problem statement, representative methods, and validation approaches, noting that understanding depends on identifiability while validation is often the hardest part.

The paper argues that unsupervised learning, including self-supervised learning, has no single goal or single maximized objective function, and proposes four distinct goals that are partly orthogonal or even conflicting: estimating the distribution, generating new data points, extracting features for downstream tasks, and understanding the data; for each goal it gives a problem statement, representative methods, and validation approaches, noting that understanding depends on identifiability while validation is often the hardest part.