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A novel gradient foster shared-representation convolutional network optimization for multi-modalities
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A novel gradient foster shared-representation convolutional network optimization for multi-modalities
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Journal Article
A novel gradient foster shared-representation convolutional network optimization for multi-modalities
2021
Overview
Significant growth has been made with multi-modal data as its entrance in the field of deep learning; whereas, Convolutional Neural Network (CNN) provides sufficient training data to develop a representative encrusted image. Yet, the multi-modality approach in CNN affect the performance by slowly converge the variance along with high-dimensionality, heterogeneity and non-aconvex optimization problems. To abridge these issues, a novel Gradient Foster Shared-representation Convolutional Network (GFSCN) framework is proposed, which improve and optimize the performance interms of accuracy and dimensionality reduction. Initially, the framework incorporates a multiple scant weighted de-noising autoencoder to solve the heterogeneity problem and reduces the dimensionality of data by transforming shared feature representation. Consequently, the work integrated enhanced stochastic variance reduced ascension approach. This approach diminishes the non-convex optimization problem through integrating two gradients consuming mini-batches, which reduced the loss function thereby achieves faster convergence even with the usage of larger dataset. Thus, the proposed framework achieves better performance in terms of achieving utmost accuracy with faster convergence and reduced variance.
Publisher
Springer US,Springer Nature B.V
