Patent Document ID: 10049323
Application ID: 15783367
Patent Flag: 1

Claim One:
1. A method of learning parameters of a convolutional neural network (“CNN”) in a computing device having a processor in which (i) activation operation of an activation module including multiple element bias layers, a scale layer, and multiple element activation layers and (ii) convolution operation of a convolutional layer are performed, comprising steps of: (a) individual multiple element bias layers receiving an input value corresponding to the input image and then each of the individual multiple element bias layers applying its corresponding each element bias parameter qi to the input value; (b) the scale layer connected to a specific element bias layer among the multiple element bias layers multiplying a predetermined scale value by an output value of the specific element bias layer; (c) a specific element activation layer connected to the scale layer applying a nonlinear activation function to an output value of the scale layer, and (ii) the other individual element activation layers connected to the individual element bias layers applying individual nonlinear functions to output values of the individual element bias layers; (d) a concatenation layer concatenating an output value of the specific element activation layer and output values of the other element activation layers, thereby acquiring a concatenated output; (e) the convolutional layer applying the convolution operation to the concatenated output by using each element weight parameter pi of the convolutional layer and each element bias parameter d thereof; (f) if the output of the step of (e) is inputted in an application block and then a result value outputted from the application block is acquired, a loss layer acquiring a loss calculated by comparing between the result value outputted from the application block and a value of Ground Truth (“GT”) corresponding thereto, thereby adjusting at least some among each element bias parameter qi of the individual element bias layers, the element weight parameter pi, and the element bias parameter d during a backpropagation process; and (g) acquiring and outputting to a storage device, the adjusted at least some among each element bias parameter qi of the individual element bias layers, the element weight parameter pi, and the element bias parameter d.