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Methods › Computer Vision › Image Model Blocks › Ghost Bottleneck

Ghost Bottleneck

24 papers tagged archive 2025-07-28

Introduced by Kai Han et al. in GhostNet: More Features from Cheap Operations

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

A Ghost BottleNeck is a skip connection block, similar to the basic residual block in ResNet in which several convolutional layers and shortcuts are integrated, but stacks Ghost Modules instead (two stacked Ghost modules). It was proposed as part of the GhostNet CNN architecture.

The first Ghost module acts as an expansion layer increasing the number of channels. The ratio between the number of the output channels and that of the input is referred to as the expansion ratio. The second Ghost module reduces the number of channels to match the shortcut path. Then the shortcut is connected between the inputs and the outputs of these two Ghost modules. The batch normalization (BN) and ReLU nonlinearity are applied after each layer, except that ReLU is not used after the second Ghost module as suggested by MobileNetV2. The Ghost bottleneck described above is for stride=1. As for the case where stride=2, the shortcut path is implemented by a downsampling layer and a depthwise convolution with stride=2 is inserted between the two Ghost modules. In practice, the primary convolution in Ghost module here is pointwise convolution for its efficiency.

PaperSourceSee Code · iamhankai/ghostnet.pytorch

Papers archive 2025-07-28

24 shown of 24, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 40 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Image Classification8
Object Detection5
image-classification5
Defect Detection3
Insulator Defect Detection3
object-detection3
GPU2
Model Compression2
Segmentation2
Semantic Segmentation2
Small Object Detection2
Acoustic Scene Classification1
Action Recognition1
CPU1
Data Augmentation1
Denoising1
Depth Estimation1
EEG1
Emotion Recognition1
Human Detection1

Usage over time archive 2025-07-28

Papers per year tagged with Ghost Bottleneck: 2019 to 2024, peak 7 7 0 2019: 1 paper 2019 2020: 4 papers 2020 2021: 5 papers 2021 2022: 7 papers 2022 2023: 1 paper 2023 2024: 6 papers 2024
Papers per year the archive tags with this method, by the paper's archive date (24 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Image Model BlocksSkip Connection Blocks

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