A neural network recognition method includes obtaining a first neural network that includes layers and a second neural network that includes a layer connected to the first neural network, actuating a processor to compute a first feature map from input data based on a layer of the first neural network, compute a second feature map from the input data based on the layer connected to the first neural network in the second neural network, and generate a recognition result based on the first neural network from an intermediate feature map computed by applying an element-wise operation to the first feature map and the second feature map.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A neural network recognition method, comprising: obtaining a first neural network comprising layers and a second neural network comprising a layer connected to the first neural network; actuating a processor to: compute a first feature map from input data based on a layer of the first neural network; compute a second feature map from the input data based on the layer connected to the first neural network in the second neural network; and generate a recognition result based on the first neural network from an intermediate feature map computed by applying an element-wise operation to the first feature map and the second feature map. 2. The neural network recognition method of claim 1, wherein the computing of the first feature map comprises computing the first feature map corresponding to the input data based on a previous layer of a target layer included in the first neural network. 3. The neural network recognition method of claim 2, wherein the generating comprises computing the recognition result from the intermediate feature map based on a next layer of the target layer included in the first neural network. 4. The neural network recognition method of claim 1, wherein the computing of the second feature map comprises computing the second feature map corresponding to the input data based on a layer connected to a target layer included in the first neural network, among a plurality of layers included in the second neural network, and transmitting the second feature map to the first neural network. 5. The neural network recognition method of claim 1, further comprising: preprocessing the second feature map and transmitting the preprocessed second feature map to the first neural network. 6. The neural network recognition method of claim 1, further comprising: generating a recognition result from the input data based on the second neural network. 7. The neural network recognition method of claim 1, wherein a number of nodes included in a layer of the first neural network is equal to a number of nodes included in the layer connected to the first neural network. 8. The neural network recognition method of claim 1, further comprising: computing a third feature map corresponding to at least one of the plurality of layers in the first neural network, and transmitting the third feature map to a third neural network. 9. The neural network recognition method of claim 1, further comprising: computing a feature map of a target layer included in the first neural network based on the target layer from a feature map of a previous layer included in the first neural network in response to the target layer being connected to the previous layer. 10. The neural network recognition method of claim 1, wherein the generating comprises: computing resulting elements by applying the element-wise operation to an individual element of the first feature map and an element corresponding to the individual element in the second feature map; and generating a set of the resulting elements as the intermediate feature map. 11. A training method for neural network recognition, the training method comprising: obtaining a first neural network comprising layers and a second neural network comprising a layer connected to the first neural network; actuating a processor to: apply an element-wise operation of a first feature map computed from a training input based on a layer of the first neural network and a second feature map computed from the training input based on the layer connected to the first neural network in the second neural network; and train the first neural network and the second neural network based on a first training recognition result computed by the first neural network through a result of the element-wise operation and a first training output corresponding to the training input. 12. The training method of claim 11, wherein the training comprises back-propagating a loss corresponding to the first training recognition result and the first training output from an output layer to an input layer of the first neural network. 13. The training method of claim 11, wherein the training comprises back-propagating a loss corresponding to the first training recognition result and the first training output to the second neural network through the layer connected to the first neural network. 14. The training method of claim 11, further comprising: training the second neural network based on a second training recognition result computed by the second neural network from the training input and a second training output corresponding to the training input. 15. The training method of claim 11, wherein the applying comprises preprocessing the second feature map, and applying the element-wise operation to the first feature map and the preprocessed second feature map. 16. The training method of claim 11, wherein the first neural network and the second neural network comprise the same layer structures and parameters. 17. The training method of claim 11, wherein a number of nodes included in a layer of the first neural network is equal to a number of nodes included in the layer connected to the first neural network. 18. The training method of claim 11, wherein the training comprises training the first neural network based on a third training recognition result computed by a third neural network and a third training output corresponding to the training input. 19. A neural network recognition method, comprising: obtaining a neural network comprising layers; actuating a processor to: compute a feature map corresponding to input data from one of the layers; apply an element-wise operation to the feature map; and compute a recognition result from a result of the element-wise operation based on at least one of the layers. 20. A non-transitory computer-readable storage medium storing program instructions that, when executed by a processor, cause the processor to perform the method of claim 1.