  In representation learning, a disentangled representation is highly desirable
as it encodes generative factors of data in a separable and compact pattern.
Researchers have advocated leveraging disentangled representations to complete
downstream tasks with encouraging empirical evidence. This paper further
investigates the necessity of disentangled representation in downstream
applications. Specifically, we show that dimension-wise disentangled
representations are unnecessary on a fundamental downstream task, abstract
visual reasoning. We provide extensive empirical evidence against the necessity
of disentanglement, covering multiple datasets, representation learning
methods, and downstream network architectures. Furthermore, our findings
suggest that the informativeness of representations is a better indicator of
downstream performance than disentanglement. Finally, the positive correlation
between informativeness and disentanglement explains the claimed usefulness of
disentangled representations in previous works. The source code is available at
https://github.com/Richard-coder-Nai/disentanglement-lib-necessity.git.
