Moreover, the calculation of output o h are restricted to the a single individual subspace, overlooking the richness of contexts and the dependencies among groups of features, which have proven beneficial to the feature learning (Ngiam et al., 2011;Wu and He, 2018).
cNNs, revealing that extracting local features with dynamic weights is superior to assigning fixed parameters.