Ye, Fei and Bors, Adrian Gheorghe orcid.org/0000-0001-7838-0021 (2020) Learning latent representations across multiple data domains using Lifelong VAEGAN. In: European Conference in Computer Vision (ECCV). Lecture Notes in Computer Science . Springer , Manchester, UK , pp. 777-795.
Abstract
The problem of catastrophic forgetting occurs in deep learning models trained on multiple databases in a sequential manner. Recently, generative replay mechanisms (GRM), have been proposed to reproduce previously learned knowledge aiming to reduce the forgetting. However, such approaches lack an appropriate inference model and therefore can not provide latent representations of data. In this paper, we propose a novel lifelong learning approach, namely the Lifelong VAEGAN (L-VAEGAN), which not only induces a powerful generative replay network but also learns meaningful latent representations, benefiting representation learning. L-VAEGAN can allow to automatically embed the information associated with different domains into several clusters in the latent space, while also capturing semantically meaningful shared latent variables, across different data domains. The proposed model supports many downstream tasks that traditional generative replay methods can not, including interpolation and inference across different data domains.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Springer Nature Switzerland AG 2020. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details. |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 30 Nov 2020 11:10 |
Last Modified: | 01 Feb 2025 00:03 |
Published Version: | https://doi.org/10.1007/978-3-030-58565-5_46 |
Status: | Published |
Publisher: | Springer |
Series Name: | Lecture Notes in Computer Science |
Identification Number: | 10.1007/978-3-030-58565-5_46 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:168546 |
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Description: Learning latent representations across multiple data domains using Lifelong VAEGAN