Representation learning using step-based deep multi-modal autoencoders

Deep learning techniques have been successfully used in learning a common representation for multi-view data, wherein different modalities are projected onto a common subspace. In a broader perspective, the techniques used to investigate common representation learning falls under the categories of ‘...

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Veröffentlicht in:Pattern recognition Jg. 95; S. 12 - 23
Hauptverfasser: Bhatt, Gaurav, Jha, Piyush, Raman, Balasubramanian
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.11.2019
Schlagworte:
Convolution autoencoders
Multilingual document classification
Representation learning
Transfer learning
Representation learning
Convolution autoencoders
Transfer learning
Multilingual document classification
ISSN:0031-3203, 1873-5142
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Zusammenfassung:Deep learning techniques have been successfully used in learning a common representation for multi-view data, wherein different modalities are projected onto a common subspace. In a broader perspective, the techniques used to investigate common representation learning falls under the categories of ‘canonical correlation-based’ approaches and ‘autoencoder-based’ approaches. In this paper, we investigate the performance of deep autoencoder-based methods on multi-view data. We propose a novel step-based correlation multi-modal deep convolution neural network (CorrMCNN) which reconstructs one view of the data given the other while increasing the interaction between the representations at each hidden layer or every intermediate step. The idea of step reconstruction reduces the constraint of reconstruction of original data, instead, the objective function is optimized for reconstruction of representative features. This helps the proposed model to generalize for representation and transfer learning tasks efficiently for high dimensional data. Finally, we evaluate the performance of the proposed model on three multi-view and cross-modal problems viz., audio articulation, cross-modal image retrieval and multilingual (cross-language) document classification. Through extensive experiments, we find that the proposed model performs much better than the current state-of-the-art deep learning techniques on all three multi-view and cross-modal tasks.
ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2019.05.032