Image Classification via Random Dilated Convolution with Multi-Branch Feature Extraction and Context Excitation

TL;DR

This paper introduces RDCNet, a novel CNN architecture that enhances image classification by integrating multi-scale feature extraction, noise robustness, and dynamic feature emphasis, achieving state-of-the-art results across multiple datasets.

Contribution

The paper presents RDCNet, combining multi-branch dilated convolutions, fine-grained feature enhancement, and context excitation modules to improve classification accuracy and robustness.

Findings

RDCNet outperforms existing methods on CIFAR-10 and CIFAR-100.

Achieves significant accuracy improvements on SVHN, Imagenette, and Imagewoof.

Demonstrates robustness against noise and irrelevant background interference.

Abstract

Image classification remains a fundamental yet challenging task in computer vision, particularly when fine-grained feature extraction and background noise suppression are required simultaneously. Conventional convolutional neural networks, despite their remarkable success in hierarchical feature learning, often struggle with capturing multi-scale contextual information and are susceptible to overfitting when confronted with noisy or irrelevant image regions. In this paper, we propose RDCNet (Image Classification Network with Random Dilated Convolution), a novel architecture built upon ResNet-34 that integrates three synergistic innovations to address these limitations: (1) a Multi-Branch Random Dilated Convolution (MRDC) module that employs parallel branches with varying dilation rates combined with a stochastic masking mechanism to capture fine-grained features across multiple scales while enhancing robustness against noise and overfitting; (2) a Fine-Grained Feature Enhancement (FGFE) module embedded within MRDC that bridges global contextual information with local feature representations through adaptive pooling and bilinear interpolation, thereby amplifying sensitivity to subtle visual patterns; and (3) a Context Excitation (CE) module that leverages softmax-based spatial attention and channel recalibration to dynamically emphasize task-relevant features while suppressing background interference. Extensive experiments conducted on five benchmark datasets -- CIFAR-10, CIFAR-100, SVHN, Imagenette, and Imagewoof -- demonstrate that RDCNet consistently achieves state-of-the-art classification accuracy, outperforming the second-best competing methods by margins of 0.02\%, 1.12\%, 0.18\%, 4.73\%, and 3.56\%, respectively, thereby validating the effectiveness and generalizability of the proposed approach across diverse visual recognition scenarios.