Abstract: A computer-implemented method comprising: obtaining an output from each of a plurality of kernels in an extraction layer of a first trained convolutional neural network, wherein the first convolutional neural network is configured to identify one or more features in an image; aggregating the outputs corresponding to at least some input samples of a first domain to generate an aggregate map corresponding to that kernel; resizing the aggregate maps to a lower resolution to generate a plurality of region maps corresponding to the aggregate maps, respectively; clustering the region maps to generate clusters of region maps, each cluster comprising region maps having similar regions; and training, using input samples of a second domain, a second convolutional neural network with a kernel weight of at least one of the kernels which corresponds to at least one of the image regions of at least one of the clusters.