An apparatus for performing domain adaptation from a source domain to a target domain, the apparatus comprising: at least one processor; and at least one computer storage that is not a transitory signal and that comprises instructions executable by the at least one processor to: access a first neural network, the first neural network being associated with a first data type; access a second neural network, the second neural network being associated with a second data type different from the first data type; provide, as input, first training data from the source domain to the first neural network, the first training data comprising image, video, or audio data; provide, as input, second training data from the target domain to the second neural network, the second training data comprising image, video, or audio data, the first training data being different from the second training data, wherein the first training data and the second training data are related, wherein the first and second training data pertain to a similar concept or metaphor in the source and target domains; identify a first output from an intermediate layer of the first neural network, the first output being based on the first training data; identify a second output from an intermediate layer of the second neural network, the second output being based on the second training data, the respective intermediate layers of the first and second neural networks being parallel layers, the parallel layers performing the same one or more tasks; identify a ratio to normalize the first output and the second output; apply an equation that accounts for the ratio to change one or more weights of the intermediate layer of the second neural network to minimize the difference in the first and second outputs, and thereby optimize the second neural network for the second data type. at least one processor; and at least one computer storage that is not a transitory signal and that comprises instructions executable by the at least one processor to: access a first neural network, the first neural network being associated with a first data type; access a second neural network, the second neural network being associated with a second data type different from the first data type; provide, as input, first training data from the source domain to the first neural network, the first training data comprising image, video, or audio data; provide, as input, second training data from the target domain to the second neural network, the second training data comprising image, video, or audio data, the first training data being different from the second training data, wherein the first training data and the second training data are related, wherein the first and second training data pertain to a similar concept or metaphor in the source and target domains; identify a first output from an intermediate layer of the first neural network, the first output being based on the first training data; identify a second output from an intermediate layer of the second neural network, the second output being based on the second training data, the respective intermediate layers of the first and second neural networks being parallel layers, the parallel layers performing the same one or more tasks; identify a ratio to normalize the first output and the second output; apply an equation that accounts for the ratio to change one or more weights of the intermediate layer of the second neural network to minimize the difference in the first and second outputs, and thereby optimize the second neural network for the second data type. access a first neural network, the first neural network being associated with a first data type; access a second neural network, the second neural network being associated with a second data type different from the first data type; provide, as input, first training data from the source domain to the first neural network, the first training data comprising image, video, or audio data; provide, as input, second training data from the target domain to the second neural network, the second training data comprising image, video, or audio data, the first training data being different from the second training data, wherein the first training data and the second training data are related, wherein the first and second training data pertain to a similar concept or metaphor in the source and target domains; identify a first output from an intermediate layer of the first neural network, the first output being based on the first training data; identify a second output from an intermediate layer of the second neural network, the second output being based on the second training data, the respective intermediate layers of the first and second neural networks being parallel layers, the parallel layers performing the same one or more tasks; identify a ratio to normalize the first output and the second output; apply an equation that accounts for the ratio to change one or more weights of the intermediate layer of the second neural network to minimize the difference in the first and second outputs, and thereby optimize the second neural network for the second data type.