Combining 2 Neural Networks
Depthwise separable convolution followed by 1x1 convolution is an alternative to the convenient 2d convolution. They were introduced in https://arxiv.org/abs/1704.04861 to aiming on reducing the computational effort. The combination of depthwise and pointwise convolutions often learn pretty much the same representation as the normal conv.
Depthwise separable conv is an extreme case of grouped conv, where num_groups=num_input_channels. Setting number of groups to 2 means slicing the input into two inputs across the channel dim and learning two separate filters for each sliced input. Here is nice tutorial on that. The output is combined by stacking each group's output. Often this is followed by 1x1 conv to mix the data flow between the groups.