Towards a New Interpretation of Separable Convolutions

TL;DR

This paper proposes a hybrid interpretative model to better understand why separable convolutions are effective in deep neural networks, addressing gaps in existing explanations.

Contribution

It introduces a new hybrid interpretation of separable convolutions, aiming to clarify their mechanism of action beyond current mathematical definitions.

Findings

Proposes a hybrid interpretation model for separable convolutions

Addresses limitations of previous explanations like the Inception hypothesis

Provides a more comprehensive understanding of separable convolutions' efficacy

Abstract

In recent times, the use of separable convolutions in deep convolutional neural network architectures has been explored. Several researchers, most notably (Chollet, 2016) and (Ghosh, 2017) 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 are still not fully understood. Although their mathematical definition is well understood as a depthwise convolution followed by a pointwise convolution, deeper interpretations such as the extreme Inception hypothesis (Chollet, 2016) 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.