Disentangling Factors of Variation Using Few Labels

Understand

Learning disentangled representations is considered a cornerstone problem in representation learning.

• Recently, Locatello et al.
• (2019) demonstrated that unsupervised disentanglement learning without inductive biases is theoretically impossible and that existing inductive biases and unsupervised methods do not allow to consistently learn disentangled representations.
• However, in many practical settings, one might have access to a limited amount of supervision, for example through manual labeling of (some) factors of variation in a few training examples.