Rethinking Depthwise Separable Convolutions: How Intra-Kernel Correlations Lead to Improved MobileNets (2003.13549v3)

Abstract: We introduce blueprint separable convolutions (BSConv) as highly efficient building blocks for CNNs. They are motivated by quantitative analyses of kernel properties from trained models, which show the dominance of correlations along the depth axis. Based on our findings, we formulate a theoretical foundation from which we derive efficient implementations using only standard layers. Moreover, our approach provides a thorough theoretical derivation, interpretation, and justification for the application of depthwise separable convolutions (DSCs) in general, which have become the basis of many modern network architectures. Ultimately, we reveal that DSC-based architectures such as MobileNets implicitly rely on cross-kernel correlations, while our BSConv formulation is based on intra-kernel correlations and thus allows for a more efficient separation of regular convolutions. Extensive experiments on large-scale and fine-grained classification datasets show that BSConvs clearly and consistently improve MobileNets and other DSC-based architectures without introducing any further complexity. For fine-grained datasets, we achieve an improvement of up to 13.7 percentage points. In addition, if used as drop-in replacement for standard architectures such as ResNets, BSConv variants also outperform their vanilla counterparts by up to 9.5 percentage points on ImageNet. Code and models are available under https://github.com/zeiss-microscopy/BSConv.

• The paper introduces BSConv as a new convolution design leveraging intra-kernel correlations to reduce redundancy and enhance MobileNet efficiency.
• It employs both theoretical and empirical analyses, showing performance gains of up to 13.7 percentage points on fine-grained datasets and 9.5 on ImageNet.
• These findings suggest that dynamically tailored convolution layers can optimize resource-limited neural networks for mobile and embedded systems.

An Expert Analysis of "Rethinking Depthwise Separable Convolutions: How Intra-Kernel Correlations Lead to Improved MobileNets"

Convolutional Neural Networks (CNNs) have been foundational in advancing computer vision tasks, emphasizing both accuracy and computational efficiency. This paper by Haase and Amthor presents blueprint separable convolutions (BSConv), a refined approach for designing CNN architectures that leverages intra-kernel correlations over the traditional depthwise separable convolutions (DSCs). The authors argue that finer efficiencies can be achieved by acknowledging redundancies along the depth axis of convolutional kernels.

Key Contributions

The paper introduces BSConv as an efficient substitute for standard convolutional layers. The authors' methodology involves theoretical as well as empirical analyses of kernel correlations in trained CNN models, revealing significant intra-depth redundancies that are often overlooked in current architectures. Unlike conventional DSCs which exploit cross-kernel correlations, BSConv is designed around intra-kernel dynamics, capitalizing on kernel depth scalar distributions to optimize processing layers in CNNs such as MobileNets.

Numerical Findings

The BSConv was rigorously tested against depthwise separable convolution-based models, demonstrating significant performance enhancements. For example, the implementation of BSConv in MobileNets yielded improvements of up to 13.7 percentage points on fine-grained datasets without complicating the architecture. On large-scale datasets like ImageNet, BSConv achieved gains of up to 9.5 percentage points over standard models, substantiating the efficacy of restructured convolutional layers.

Implications and Future Directions

The paper's findings cannot be understated in practical applications, especially in resource-constrained environments like mobile and embedded systems where model efficiency is paramount. By showing that BSConv can outperform and reduce computational demands over traditional methods, this work suggests that dynamically tailored convolution layers should be considered a critical aspect of future neural architecture designs.

On a theoretical plane, the implications extend toward refining transfer learning strategies, as precision gains in CNNs often dictate successful model adaptations in varying data domains with limited computational resources. Future directions might include the integration of BSConv with other architecture search paradigms, or an exploration of these concepts in architectures beyond vision tasks.

Overall, this paper underscores the importance of intra-kernel correlations and provides a rigorous framework to conceptualize and implement these insights effectively. The advances presented in this work are poised to influence both the current state-of-the-art applications and the foundational methodologies underpinning neural network design. As AI continues to advance, the insights from BSConv will likely inspire novel research trajectories, ensuring more nuanced understandings of convolutional operations within neural networks.