TY - CONF
T1 - UNSUPERVISED DISENTANGLEMENT WITH TENSOR PRODUCT REPRESENTATIONS ON THE TORUS
AU - Rotman, Michael
AU - Dekel, Amit
AU - Gur, Shir
AU - Oz, Yaron
AU - Wolf, Lior
N1 - Publisher Copyright:
© 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.
PY - 2022
Y1 - 2022
N2 - The current methods for learning representations with auto-encoders almost exclusively employ vectors as the latent representations. In this work, we propose to employ a tensor product structure for this purpose. This way, the obtained representations are naturally disentangled. In contrast to the conventional variations methods, which are targeted toward normally distributed features, the latent space in our representation is distributed uniformly over a set of unit circles. We argue that the torus structure of the latent space captures the generative factors effectively. We employ recent tools for measuring unsupervised disentanglement, and in an extensive set of experiments demonstrate the advantage of our method in terms of disentanglement, completeness, and informativeness. The code for our proposed method is available at https://github.com/rotmanmi/Unsupervised-Disentanglement-Torus.
AB - The current methods for learning representations with auto-encoders almost exclusively employ vectors as the latent representations. In this work, we propose to employ a tensor product structure for this purpose. This way, the obtained representations are naturally disentangled. In contrast to the conventional variations methods, which are targeted toward normally distributed features, the latent space in our representation is distributed uniformly over a set of unit circles. We argue that the torus structure of the latent space captures the generative factors effectively. We employ recent tools for measuring unsupervised disentanglement, and in an extensive set of experiments demonstrate the advantage of our method in terms of disentanglement, completeness, and informativeness. The code for our proposed method is available at https://github.com/rotmanmi/Unsupervised-Disentanglement-Torus.
UR - http://www.scopus.com/inward/record.url?scp=85140363383&partnerID=8YFLogxK
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AN - SCOPUS:85140363383
T2 - 10th International Conference on Learning Representations, ICLR 2022
Y2 - 25 April 2022 through 29 April 2022
ER -