A Commentary on the Unsupervised Learning of Disentangled Representations
This commentary addresses the foundational problem of disentanglement in machine learning for researchers and practitioners, emphasizing incremental insights from existing work.
The paper summarizes a prior study showing that unsupervised learning of disentangled representations is theoretically impossible without inductive biases, and it highlights practical limitations and future research directions based on experimental findings.
The goal of the unsupervised learning of disentangled representations is to separate the independent explanatory factors of variation in the data without access to supervision. In this paper, we summarize the results of Locatello et al., 2019, and focus on their implications for practitioners. We discuss the theoretical result showing that the unsupervised learning of disentangled representations is fundamentally impossible without inductive biases and the practical challenges it entails. Finally, we comment on our experimental findings, highlighting the limitations of state-of-the-art approaches and directions for future research.