On the Necessity of Disentangled Representations for Downstream Tasks

Ruiqian Nai, Zixin Wen, Ji Li, Yuanzhi Li, Yang Gao

Keywords: representation disentanglement, representation learning downstream task

Abstract: A disentangled representation encodes generative factors of data in a separable and compact pattern. Thus it is widely believed that such a representation format benefits downstream tasks. In this paper, we challenge the necessity of disentangled representation in downstream applications. Specifically, we show that dimension-wise disentangled representations are not necessary for downstream tasks using neural networks that take learned representations as input. We provide extensive empirical evidence against the necessity of disentanglement, covering multiple datasets, representation learning methods, and downstream network architectures. Moreover, our study reveals that the informativeness of representations best accounts for downstream performance. The positive correlation between informativeness and disentanglement explains the claimed usefulness of disentangled representations in previous works.

Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.

No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics

Submission Guidelines: Yes

Please Choose The Closest Area That Your Submission Falls Into: Deep Learning and representational learning

TL;DR: We show that dimension-wise disentangled representations are not necessary for downstream tasks using deep neural networks with learned representations as input.