I'm looking for an efficient way to do convolution when the input images/feature maps are sparse.
So you'd have a sparse input (n_samples, n_features_in, n_rows, n_cols), a dense kernel (n_features_out, n_features_in, n_kernel_rows, n_kernel_cols), and produce either a sparse or dense output of shape (n_samples, n_features_out, n_rows +n_kernel_rows-1, n_cols+n_kernel_cols-1). I've seen theano's sparse sandbox <http://deeplearning.net/software/theano/library/sparse/sandbox.html>, but it's not obvious from here that it would support this kind of operation (it seems to be more about implementing dense convolutions as sparse matrix multiplications). Does anybody know of existing code that deals with this situation? -- --- You received this message because you are subscribed to the Google Groups "theano-users" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. For more options, visit https://groups.google.com/d/optout.
