Learning generative models across incomparable spaces
© 36th International Conference on Machine Learning, ICML 2019. All rights reserved. Generative Adversarial Networks have shown remarkable success in learning a distribution that faithfully recovers a reference distribution in its entirety. However, in some cases, we may want to only learn some aspe...
Main Authors: | Bunne, C, Alvarez-Melis, D, Krause, A, Jegelka, S |
---|---|
Format: | Article |
Language: | English |
Published: |
2021
|
Online Access: | https://hdl.handle.net/1721.1/132307 |
Similar Items
Similar Items
-
Learning generative models across incomparable spaces
by: Bunne, Charlotte, et al.
Published: (2022) -
The incomparable artist
by: Tessel M. Bauduin
Published: (2022-09-01) -
Incomparability and practical reason
by: Chang, R, et al.
Published: (1997) -
Comparer l'incomparable
by: Éric Duchemin -
Des terrains incomparés ?
by: Marie David
Published: (2021-12-01)