RegNet: Self-Regulated Network for Image Classification (2101.00590v1)

Abstract: The ResNet and its variants have achieved remarkable successes in various computer vision tasks. Despite its success in making gradient flow through building blocks, the simple shortcut connection mechanism limits the ability of re-exploring new potentially complementary features due to the additive function. To address this issue, in this paper, we propose to introduce a regulator module as a memory mechanism to extract complementary features, which are further fed to the ResNet. In particular, the regulator module is composed of convolutional RNNs (e.g., Convolutional LSTMs or Convolutional GRUs), which are shown to be good at extracting Spatio-temporal information. We named the new regulated networks as RegNet. The regulator module can be easily implemented and appended to any ResNet architecture. We also apply the regulator module for improving the Squeeze-and-Excitation ResNet to show the generalization ability of our method. Experimental results on three image classification datasets have demonstrated the promising performance of the proposed architecture compared with the standard ResNet, SE-ResNet, and other state-of-the-art architectures.

• The paper introduces a ConvRNN-based regulator module that enhances ResNet feature extraction by capturing complementary spatio-temporal information.
• Experimental results show error reductions of 1.51% on CIFAR-10, 2.04% on CIFAR-100, and improved ImageNet top-1 and top-5 accuracies compared to standard ResNets.
• The study paves the way for integrating hybrid residual-recurrent architectures in diverse applications, including object detection and image super-resolution.

RegNet: Self-Regulated Network for Image Classification

The paper entitled "RegNet: Self-Regulated Network for Image Classification" introduces an innovative approach to enhancing the feature extraction capabilities of residual networks (ResNets), which are widely recognized for their effectiveness in image classification tasks. The authors identify a key limitation in ResNet architectures, namely the additive function's confinement to a shortcut connection mechanism, which restricts the exploration of complementary features. To mitigate this, they propose RegNet, a self-regulated network that incorporates a regulator module composed of convolutional recurrent neural networks (ConvRNNs) to facilitate enhanced spatio-temporal feature extraction.

The authors argue that, despite ResNet's success with its shortcut connections allowing for efficient gradient flow and deep network training, the mechanism fails to exploit potentially reusable information learned in earlier building blocks. Their solution introduces ConvRNNs—specifically, Convolutional LSTMs and GRUs—as a parallel memory mechanism that aids supplementary feature extraction. The regulator module is designed to process the feature maps of each building block while maintaining gradient flow by utilizing this modular architecture.

Experimental results are presented from three prominent image classification benchmarks: CIFAR-10, CIFAR-100, and ImageNet. The findings indicate a measurable improvement in classification accuracy compared to standard ResNet, SE-ResNet, and other competitive architectures. Notably, RegNet, when employing ConvLSTM, exhibited a decrease in error rates by 1.51% on CIFAR-10 and 2.04% on CIFAR-100, demonstrating its effectiveness in capturing additional spatial features. Further experimentation on ImageNet shows that RegNet surpasses ResNet-50, with test results indicating a 1.38% improvement in top-1 error and 0.85% in top-5 error.

The paper discusses theoretical implications of the RegNet model as a critical enhancement to the existing ResNet paradigm. This approach not only leverages historical feature representation through recurrent networks but also indicates potential paths for further reducing network depth while maintaining performance. As residual networks are foundational in numerous computer vision tasks, the ability to refine them through regulators such as ConvRNNs could stimulate further research into hybrid models combining residual and recurrent architectures.

In terms of future research trajectories, the authors suggest expanding the application of the regulator module to other ResNet-based models like SE-ResNet, Wide ResNet, and Inception-ResNet, among others. Moreover, exploring RegNet’s capabilities in other domains such as object detection and image super-resolution could yield further insights and advancements in deep learning technology application.

The methodological innovations brought forth by this paper provide valuable insights on enhancing deep neural network architectures by integrating more sophisticated feature extraction mechanisms, expanding both theoretical understanding and practical application in image classification tasks.