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Convolutional Neural Networks need the construction of informative features,which are determined by channel-wise and spatial-wise information at thenetwork's layers. In this research, we focus on bringing in a novel solutionthat uses sophisticated optimization for enhancing both the spatial and channelcomponents inside each layer's receptive field. Capsule Networks were used tounderstand the spatial association between features in the feature map.Standalone capsule networks have shown good results on comparatively simpledatasets than on complex datasets as a result of the inordinate amount offeature information. Thus, to tackle this issue, we have proposed ME-CapsNet byintroducing deeper convolutional layers to extract important features beforepassing through modules of capsule layers strategically to improve theperformance of the network significantly. The deeper convolutional layerincludes blocks of Squeeze-Excitation networks which use a stochastic samplingapproach for progressively reducing the spatial size thereby dynamicallyrecalibrating the channels by reconstructing their interdependencies withoutmuch loss of important feature information. Extensive experimentation was doneusing commonly used datasets demonstrating the efficiency of the proposedME-CapsNet, which clearly outperforms various research works by achievinghigher accuracy with minimal model complexity in complex datasets.

ME-CapsNet: A Multi-Enhanced Capsule Networks with Routing Mechanism