Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning

Understand

Nonlinear ICA is a fundamental problem for unsupervised representation learning, emphasizing the capacity to recover the underlying latent variables generating the data (i.e., identifiability).

• Recently, the very first identifiability proofs for nonlinear ICA have been proposed, leveraging the temporal structure of the independent components.
• Here, we propose a general framework for nonlinear ICA, which, as a special case, can make use of temporal structure.
• It is based on augmenting the data by an auxiliary variable, such as the time index, the history of the time series, or any other available information.