Methods › Computer Vision › Image Model Blocks › ENet Bottleneck

ENet Bottleneck

24 papers tagged archive 2025-07-28

Introduced by Adam Paszke et al. in ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation

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

ENet Bottleneck is an image model block used in the ENet semantic segmentation architecture. Each block consists of three convolutional layers: a 1 × 1 projection that reduces the dimensionality, a main convolutional layer, and a 1 × 1 expansion. We place Batch Normalization and PReLU between all convolutions. If the bottleneck is downsampling, a max pooling layer is added to the main branch. Also, the first 1 × 1 projection is replaced with a 2 × 2 convolution with stride 2 in both dimensions. We zero pad the activations, to match the number of feature maps.

PaperSourceSee Code · yassouali/pytorch_segmentation

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 38 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
Semantic Segmentation13
Segmentation10
Autonomous Driving5
Autonomous Vehicles2
Decoder2
Deep Learning2
Efficient Neural Network2
Image Segmentation2
Instance Segmentation2
Medical Image Analysis2
Quantization2
Real-Time Semantic Segmentation2
Scene Understanding2
regression2
Cardiac Segmentation1
Data Augmentation1
Decision Making1
Depth Estimation1
Diagnostic1
Disaster Response1

Usage over time archive 2025-07-28

Papers per year tagged with ENet Bottleneck: 2016 to 2024, peak 5 5 0 2016: 1 paper 2016 2017: 3 papers 2017 2018: 5 papers 2018 2019: 4 papers 2019 2020: 4 papers 2020 2021: 2 papers 2021 2022: 2 papers 2022 2023: 2 papers 2023 2024: 1 paper 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 Blocks

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