In recent times, the use of separable convolutions in deep convolutional neural network architectures has been explored. Several researchers, most notably and have used separable convolutions in their deep architectures and have demonstrated state of the art or close to state of the art performance. However, the underlying mechanism of action of separable convolutions is still not fully understood. Although, their mathematical definition is well understood as a depth-wise convolution followed by a point-wise convolution, “deeper” interpretations (such as the “extreme Inception”) hypothesis have failed to provide a thorough explanation of their efficacy. In this paper, we propose a hybrid interpretation that we believe is a better model for explaining the efficacy of separable convolutions.