On top of this hidden representation z, a deconvolutional layer, which performs transposed operations of convolutions (Radford et al., 2016), is further applied to produce a unique set of filters for X (as illustrated in Figure 1): where θ e and θ d are the learned parameters in each layer of the filter-generating module, respectively.
we convolve z with a filter of size (f s , l, k x , k y ), where f s is the number of generated filters and the kernel size is (k x , k y ).