Representation learning using step-based deep multi-modal autoencoders. (November 2019)

• Record Type: Journal Article
• Authors: Bhatt, Gaurav; Jha, Piyush; Raman, Balasubramanian
• Is Part Of: Pattern recognition. Volume 95(2019:Nov.)
• Page Start: 12
• Page End: 23
• Publication Date: 2019-11
• Subjects: Representation learning -- Transfer learning -- Convolution autoencoders -- Multilingual document classification; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2019.05.032
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: 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.