A computer implemented method for learning parameters of a CNN using a 1×K convolution operation or a K×1 convolution operation, the method for learning comprising the steps of: (a) generating, via a reshaping layer, a reshaped feature map by two-dimensionally concatenating each of features in each group comprised of each corresponding K channels among all channels of a processed feature map of a training image, wherein the processed feature map has a width (W), a height (H) and a depth (L) comprising a plurality of channels, wherein each channel of the plurality of channels has a plurality of features and wherein each feature of the plurality of features corresponds to each pixel of the processed feature map, and wherein each pixel of each channel in the reshaped feature map corresponds to a one-on-one basis to each of the two-dimensionally concatenated features in said each group comprised of said each corresponding K different channels and wherein the processed feature map is reshaped to form the reshaped feature map having a width of W, a height of H*K and a channel of CEIL(L/K) by rearranging values of pixels of the processed feature map on a one-by-one basis, located in K channels, on a single channel, and the values of the pixels are maintained before and after being reshaped, and generating, via a subsequent convolutional layer, an adjusted feature map whose volume is adjusted by applying the 1×K convolution operation or the K×1 convolution operation to the reshaped feature map; and (b) generating, via an output layer, at least one output by referring to features on the adjusted feature map or its processed feature map, calculating, via a loss layer, one or more losses by referring to the output and its corresponding at least one ground truth, and learning at least part of parameters of the subsequent convolutional layer by backpropagating the losses. (a) generating, via a reshaping layer, a reshaped feature map by two-dimensionally concatenating each of features in each group comprised of each corresponding K channels among all channels of a processed feature map of a training image, wherein the processed feature map has a width (W), a height (H) and a depth (L) comprising a plurality of channels, wherein each channel of the plurality of channels has a plurality of features and wherein each feature of the plurality of features corresponds to each pixel of the processed feature map, and wherein each pixel of each channel in the reshaped feature map corresponds to a one-on-one basis to each of the two-dimensionally concatenated features in said each group comprised of said each corresponding K different channels and wherein the processed feature map is reshaped to form the reshaped feature map having a width of W, a height of H*K and a channel of CEIL(L/K) by rearranging values of pixels of the processed feature map on a one-by-one basis, located in K channels, on a single channel, and the values of the pixels are maintained before and after being reshaped, and generating, via a subsequent convolutional layer, an adjusted feature map whose volume is adjusted by applying the 1×K convolution operation or the K×1 convolution operation to the reshaped feature map; and (b) generating, via an output layer, at least one output by referring to features on the adjusted feature map or its processed feature map, calculating, via a loss layer, one or more losses by referring to the output and its corresponding at least one ground truth, and learning at least part of parameters of the subsequent convolutional layer by backpropagating the losses. <EOS>