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Associative variational auto-encoder with distributed latent spaces and associators
Cited 6 time in
Web of Science
Cited 7 time in Scopus
- Authors
- Issue Date
- 2020-01
- Publisher
- AAAI press
- Citation
- AAAI 2020 - 34th AAAI Conference on Artificial Intelligence, pp.11197-11204
- Abstract
- Copyright © 2020, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.In this paper, we propose a novel structure for a multimodal data association referred to as Associative Variational Auto-Encoder (AVAE). In contrast to the existing models using a shared latent space among modalities, our structure adopts distributed latent spaces for multi-modalities which are connected through cross-modal associators. The proposed structure successfully associates even heterogeneous modality data and easily incorporates the additional modality to the entire network via the associator. Furthermore, in our structure, only a small amount of supervised (paired) data is enough to train associators after training auto-encoders in an unsupervised manner. Through experiments, the effectiveness of the proposed structure is validated on various datasets including visual and auditory data.
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