|Appears in Collections:||Computing Science and Mathematics Conference Papers and Proceedings|
|Title:||The Variational InfoMax AutoEncoder|
|Citation:||Crescimanna V & Graham B (2020) The Variational InfoMax AutoEncoder. In: 2020 International Joint Conference on Neural Networks. IEEE International Joint Conference on Neural Networks (IJCNN) IJCNN 2020 - International Joint Conference on Neural Networks, Glasgow, UK, 19.07.2020-24.07.2020. Piscataway, NJ: IEEE. https://doi.org/10.1109/IJCNN48605.2020.9207048|
|Series/Report no.:||IEEE International Joint Conference on Neural Networks (IJCNN)|
|Conference Name:||IJCNN 2020 - International Joint Conference on Neural Networks|
|Conference Dates:||2020-07-19 - 2020-07-24|
|Conference Location:||Glasgow, UK|
|Abstract:||The Variational AutoEncoder (VAE) learns simultaneously an inference and a generative model, but only one of these models can be learned at optimum, this behaviour is associated to the ELBO learning objective, that is optimised by a non-informative generator. In order to solve such an issue, we provide a learning objective, learning a maximal informative generator while maintaining bounded the network capacity: the Variational InfoMax (VIM). The contribution of the VIM derivation is twofold: an objective learning both an optimal inference and generative model and the explicit definition of the network capacity, an estimation of the network robustness.|
|Status:||AM - Accepted Manuscript|
|Rights:||© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.|
|Crescimanna-Graham-IEEE-2020.pdf||Fulltext - Accepted Version||2.63 MB||Adobe PDF||View/Open|
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