Abstract: Deep learning operations (e.g., transposed convolution, resized convolution, dilated convolution, etc.) may be performed with sparsity maps and storage pointers. A deep learning operation has a tensor, which can be used to generate an upsampled tensor by adding new data elements (e.g., zeros) into the tensor. One or more sparsity maps may be generated based on one or more parameters of the first deep learning operation. The sparsity map may include elements indicating whether a data element in the upsampled tensor is a data element in the tensor or is a new data element. One or more storage pointers may be generated. A storage pointer may indicate a location (e.g., a memory address) where one or more data elements of the tensor are stored in a memory. An output of the deep learning operation may be performed using data elements in the tensor, the sparsity maps, and the storage pointers.