Local Binary Convolutional Neural Networks (1608.06049v2)

Abstract: We propose local binary convolution (LBC), an efficient alternative to convolutional layers in standard convolutional neural networks (CNN). The design principles of LBC are motivated by local binary patterns (LBP). The LBC layer comprises of a set of fixed sparse pre-defined binary convolutional filters that are not updated during the training process, a non-linear activation function and a set of learnable linear weights. The linear weights combine the activated filter responses to approximate the corresponding activated filter responses of a standard convolutional layer. The LBC layer affords significant parameter savings, 9x to 169x in the number of learnable parameters compared to a standard convolutional layer. Furthermore, the sparse and binary nature of the weights also results in up to 9x to 169x savings in model size compared to a standard convolutional layer. We demonstrate both theoretically and experimentally that our local binary convolution layer is a good approximation of a standard convolutional layer. Empirically, CNNs with LBC layers, called local binary convolutional neural networks (LBCNN), achieves performance parity with regular CNNs on a range of visual datasets (MNIST, SVHN, CIFAR-10, and ImageNet) while enjoying significant computational savings.

• The paper introduces a novel Local Binary Convolution layer that uses fixed binary filters to significantly reduce learnable parameters.
• It validates the approach both theoretically and empirically on benchmarks like MNIST, CIFAR-10, and ImageNet while achieving competitive accuracy.
• The method paves the way for efficient deep networks applicable to mobile and edge computing, enabling extensive network depth with reduced complexity.

An Expert Overview of "Local Binary Convolutional Neural Networks"

In the paper titled "Local Binary Convolutional Neural Networks," the authors present a novel approach to designing convolutional layers within Convolutional Neural Networks (CNNs) by proposing the Local Binary Convolution (LBC) module. This approach aims to reduce the computational complexity and parameter size of CNNs whilst maintaining, or even enhancing, performance levels across various image classification tasks.

Core Contributions and Methodology

The LBC layer represents an innovative shift from traditional convolutional layers by leveraging fixed, sparse, predefined binary filters inspired by Local Binary Patterns (LBP). Unlike conventional convolutions wherein the weights are learned during training, these binary filters, referred to as anchor weights, remain static. Their function is complemented by learnable linear weights which perform weighted summations of the non-linear activations from these fixed filters to approximate the output of a typical convolutional layer. This strategic composition not only leads to substantial reductions in the number of learnable parameters—by factors between 9x to 169x depending on filter size—but also minimizes memory footprint and computational expenditure.

The theoretical framework provided in the paper validates the approximation accuracy of the LBC layer compared to standard convolutional layers. A probabilistic demonstration establishes that the output of an LBC layer is likely to closely align with the activation of a standard convolutional layer under typical conditions. Furthermore, empirical experiments across multiple datasets (MNIST, SVHN, CIFAR-10, and ImageNet) indicate that Local Binary Convolutional Neural Networks (LBCNNs) achieve competitive accuracy levels with significantly reduced model complexity.

Experimental Results and Numerical Insights

Experiments conducted demonstrate that LBCNNs can perform on par with conventional CNNs on standardized benchmark datasets, with notable computational savings. Particularly, across the CIFAR-10 dataset, LBCNNs retained competitive accuracy even when utilizing 10x fewer parameters compared to traditional CNN architectures. The results from experiments on MNIST, SVHN, and a subset of the ImageNet dataset corroborate these findings, where LBCNNs not only match but sometimes exceed baseline CNN performances in terms of model efficiency and overfitting resistance.

Moreover, the introduction of extreme depth in networks (termed NetEverest) was rendered feasible by LBC layers, allowing for the training of networks with thousands of convolutional layers on commodity hardware. The approach to replace larger AlexNet layers with LBC modules also demonstrated the viability of scaling this methodology to large-scale datasets while maintaining accuracy comparable to traditional setups.

Implications and Future Directions

This exploration into Local Binary Convolution not only addresses the critical challenge of computational efficiency in deep learning but also opens new avenues for deploying sophisticated CNN architectures across low-resource environments, such as mobile and edge computing devices. The reduction in parameter necessity without sacrificing classification accuracy reflects a significant step towards the democratization of deep learning technologies.

Future extensions of this work could involve the exploration of adaptable sparse patterns for the binary filters to dynamically adjust to different data characteristics or tasks. Moreover, integrating LBCNNs with other lightweight model architectures such as MobileNets or EfficientNets could further enhance efficacy across diverse operational constraints.

In conclusion, the paper presents a robust framework for reducing the computational burden of CNNs using local binary convolutions, with compelling theoretical and experimental support. This innovation could fuel further advancements toward efficient and scalable neural network architectures well-suited for a broad range of real-world applications.