Devices, systems, and methods obtain data in a first modality; propagate the data in the first modality through a neural network, thereby generating network outputs, wherein the neural network includes a first-stage neural network and a second-stage neural network, wherein the first-stage neural network includes two or more layers, wherein each layer of the two or more layers of the first-stage neural network includes a plurality of respective nodes, wherein the second-stage neural network includes two or more layers, one of which is an input layer and one of which is an output layer, and wherein each node in each layer of the first-stage neural network is connected to the input layer of the second-stage neural network; calculate a gradient of a loss function based on the network outputs; backpropagate the gradient through the neural network; and update the neural network based on the backpropagation of the gradient.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A device comprising: one or more computer-readable media; and one or more processors that are coupled to the one or more computer-readable media and that are configured to cause the device to obtain data in a first modality; propagate the data in the first modality through a first neural network, thereby generating first network outputs, wherein the first neural network includes a first-stage neural network and a second-stage neural network, wherein the first-stage neural network includes two or more layers, wherein each layer of the two or more layers of the first-stage neural network includes a plurality of respective nodes, wherein the second-stage neural network includes two or more layers, one of which is an input layer and one of which is an output layer, wherein each node in each layer of the first-stage neural network is connected to the input layer of the second-stage neural network, and wherein the output layer of the second-stage neural network produces the first network outputs; calculate a gradient of a loss function based on the first network outputs and on the loss function; backpropagate the gradient of the loss function through the first neural network; and update the first neural network based on the backpropagation of the gradient. 2. The system of claim 1, wherein the one or more processors are further configured to cause the device to obtain data in a second modality, wherein the second modality is different from the first modality; propagate the data in the second modality through a second neural network, thereby producing second network outputs; and calculate the gradient of the loss function further based on the second network outputs, wherein the loss function is a cross-modality loss function. 3. The system of claim 2, wherein the second neural network is a copy of the first neural network. 4. The system of claim 2, wherein the second neural network is different from the first neural network. 5. The system of claim 4, wherein the one or more processors are further configured to cause the device to backpropagate the gradient of the loss function through the second neural network; and update the second neural network based on the backpropagation of the gradient. 6. The system of claim 1, wherein to backpropagate the gradient of the loss function through the first neural network, the one or more processors are further configured to cause the device to backpropagate the gradient through the first neural network in the following order: from the output layer of the second-stage neural network to the input layer of the second-stage neural network, then from the input layer of the second-stage neural network to each layer of the first-stage neural network, and then from a deepest layer of the first-stage neural network to an input layer of the first-stage neural network. 7. The system of claim 1, wherein the one or more processors are further configured to cause the device to enforce a sparsity constraint between the first-stage neural network and the input layer of the second-stage neural network or between the two or more layers of the second-stage neural network. 8. One or more computer-readable storage media storing computer-executable instructions that, when executed by one or more computing devices, cause the one or more computing devices to perform operations comprising: obtaining data in a first modality; propagating the data in the first modality through a first neural network, thereby generating first network outputs, wherein the first neural network includes a first-stage neural network and a second-stage neural network, wherein the first-stage neural network includes two or more layers, one of which is an input layer and one of which is a deepest layer, wherein the second-stage neural network includes two or more layers, one of which is an input layer and one of which is a deepest layer, and wherein the input layer of the second-stage neural network is fully connected to the first-stage neural network; calculating a gradient of a loss function based on the first network outputs and on the loss function; backpropagating the gradient of the loss function through the first neural network; and updating the first neural network based on the backpropagation of the gradient. 9. The one or more computer-readable media of claim 8, wherein the operations further comprise: obtaining data in a second modality, wherein the second modality is different from the first modality; and propagating the data in the second modality through a second neural network, thereby generating second network outputs, wherein the loss function is a cross-modality loss function, and wherein calculating the gradient of the loss function is further based on the second network outputs. 10. The one or more computer-readable media of claim 9, wherein cross-modality loss function imposes a cross-modal constraint, and wherein the cross-modal constraint can be described by min  ( ∑ m = 1 M   ( L m  ( f m  ( W m , X m ) , Y m ) + λ m   W m - TEN  ) + α  ∑ i = 1 M   ∑ j = 1 M   cL  ( f i  ( W i , X i ) , f j  ( W j , X j ) ) ) , where the subscripts m, i, and j indicate different modalities of data, and where α is the balancing coefficient for the constraint. 11. The one or more computer-readable media of claim 9, wherein the data in the first modality and the data in the second modality are paired. 12. The one or more computer-readable media of claim 11, wherein the data in the first modality are an RGB image of a scene, and wherein the data in the second modality are a depth image of the scene. 13. The one or more computer-readable media of claim 8, wherein backpropagating the gradient through the first neural network is performed in the following order: from the deepest layer of the second-stage neural network to the input layer of the second-stage neural network, then from the input layer of the second-stage neural network to each layer of the first-stage neural network, and then from the deepest layer of the first-stage neural network to the input layer of the first-stage neural network. 14. The one or more computer-readable media of claim 8, wherein calculating the gradient of the loss function is further based on a training target. 15. A method comprising: obtaining data in a first modality; propagating the data in the first modality through a first neural network, thereby generating first network outputs, wherein the first neural network includes a first-stage neural network and a second-stage neural network, wherein the first-stage neural network includes two or more layers, wherein each layer of the two or more layers of the first-stage neural network includes a plurality of respective nodes, wherein the second-stage neural network includes two or more layers, one of which is an input layer and one of which is an output layer, wherein each node in each layer of the first-stage neural network is connected to the input layer of the second-stage neural network, and wherein the output layer of the second-stage neural network produces the first network outputs; calculating a gradient of a loss function based on the first network outputs and on the loss function; backpropagating the gradient of the loss function through the first neural network; and updating the first neural network based on the backpropagation of the gradient. 16. The method of claim 15, further comprising: obtaining data in a second modality, wherein the second modality is different from the first modality; and propagating the data in the second modality through a second neural network, thereby producing second network outputs, wherein calculating the gradient of the loss function is further based on the second network outputs. 17. The method of claim 16, wherein the loss function includes a cross-modality loss function and a single-modality loss function. 18. The method of claim 17, wherein calculating the gradient of the loss function is further based on a training target. 19. The method of claim 15, wherein backpropagating the gradient of the loss function through the first neural network includes backpropagating the gradient through at least some of the layers of the first-stage neural network more than once. 20. The method of claim 15, wherein updating the first neural network based on the backpropagation of the gradient includes enforcing a sparsity constraint between the first-stage neural network and the input layer of the second-stage neural network or between the two or more layers of the second-stage neural network.