# Gated Convolutional Networks with Hybrid Connectivity for Image Classification

arXiv: https://arxiv.org/abs/1908.09699 · HTML: https://mlsanity.com/paper/1908.09699/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2019.

## Built on by (most cited first, 14 of 14)

- [Cross-Image Relational Knowledge Distillation for Semantic Segmentation](https://mlsanity.com/paper/2204.06986/) (2022)
- [Hierarchical Self-supervised Augmented Knowledge Distillation](https://mlsanity.com/paper/2107.13715/) (2021)
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- [Online Knowledge Distillation via Mutual Contrastive Learning for Visual Recognition](https://mlsanity.com/paper/2207.11518/) (2022)
- [MixSKD: Self-Knowledge Distillation from Mixup for Image Recognition](https://mlsanity.com/paper/2208.05768/) (2022)
- [SGLP: A Similarity Guided Fast Layer Partition Pruning for Compressing Large Deep Models](https://mlsanity.com/paper/2410.14720/) (2024)
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- [Knowledge Distillation Using Hierarchical Self-Supervision Augmented Distribution](https://mlsanity.com/paper/2109.03075/) (2021)
- [Recurrence along Depth: Deep Convolutional Neural Networks with Recurrent Layer Aggregation](https://mlsanity.com/paper/2110.11852/) (2021)
- [Online Policy Distillation with Decision-Attention](https://mlsanity.com/paper/2406.05488/) (2024)
- [Prototype-Driven Multi-Feature Generation for Visible-Infrared Person Re-identification](https://mlsanity.com/paper/2409.05642/) (2024)
- [Localizing Semantic Patches for Accelerating Image Classification](https://mlsanity.com/paper/2206.03367/) (2022)
- [GoogLe2Net: Going Transverse with Convolutions](https://mlsanity.com/paper/2301.00424/) (2023)
- [Team AcieLee: Technical Report for EPIC-SOUNDS Audio-Based Interaction Recognition Challenge 2023](https://mlsanity.com/paper/2306.08998/) (2023)

## Abstract (arXiv)

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.