# Gated Convolutional Networks with Hybrid Connectivity for Image Classification Source: [Gated Convolutional Networks with Hybrid Connectivity for Image Classification]() Collection index: [llms.txt]() Data updated: 2026-01-04T03:09:37.882Z Authors: Chuanguang Yang, Zhulin An, Hui Zhu, Xiaolong Hu, Kun Zhang, Kaiqiang Xu, Chao Li, Yongjun Xu Published: 2019-08-26T14:13:21.000Z ## Abstract We propose a simple yet effective method to reduce the redundancy of DenseNet by substantially decreasing the number of stacked modules by replacing the original bottleneck by our SMG module, which is augmented by local residual. Furthermore, SMG module is equipped with an efficient two-stage pipeline, which aims to DenseNet-like architectures that need to integrate all previous outputs, i.e., squeezing the incoming informative but redundant features gradually by hierarchical convolutions as a hourglass shape and then exciting it by multi-kernel depthwise convolutions, the output of which would be compact and hold more informative multi-scale features. We further develop a forget and an update gate by introducing the popular attention modules to implement the effective fusion instead of a simple addition between reused and new features. Due to the Hybrid Connectivity (nested combination of global dense and local residual) and Gated mechanisms, we called our network as the HCGNet. Experimental results on CIFAR and ImageNet datasets show that HCGNet is more prominently efficient than DenseNet, and can also significantly outperform state-of-the-art networks with less complexity. Moreover, HCGNet also shows the remarkable interpretability and robustness by network dissection and adversarial defense, respectively. On MS-COCO, HCGNet can consistently learn better features than popular backbones. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Object Detection on COCO 2017 (val)](): Object Detection; COCO 2017 (val); AP: 38.3. - [Image Classification on CIFAR-100 (test)](): Image Classification; CIFAR-100 (test); Accuracy: not recorded. - [Instance Segmentation on COCO 2017 (val)](): Instance Segmentation; COCO 2017 (val); APm: not recorded. - [Image Classification on CIFAR-10 (test) (Accuracy)](): Image Classification; CIFAR-10 (test); Accuracy: not recorded. - [Image Classification on ImageNet 2012 (val)](): Image Classification; ImageNet 2012 (val); Top-1 Accuracy: not recorded. - [Image Classification on ILSVRC 2012 (val)](): Image Classification; ILSVRC 2012 (val); Top-1 Accuracy: not recorded. ## Original sources and implementation [Original paper]() [PDF]()