Hi guys, I'm doing some work where I'd like to have a data-dependent convolutional layer, where a different filterbank is used on each element of a given batch. This is easy when doing things with a single batch, but for minibatches I'd end up with a 5D Batch x Num_filters x Channels x H x W tensor, and I can't think of an easy way to apply this on a 4D Batch x Channel x H x W tensor. Is there some trick with backward passes, dimshuffles and 3D reshapes, or something else that anyone knows of that would give me an efficient way to do this?
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