ENet Bottleneck

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

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.

Source: ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation

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Papers

Paper	Code	Results	Date	Stars

Tasks

Task	Papers	Share
Semantic Segmentation	13	23.21%
Autonomous Driving	5	8.93%
Image Segmentation	2	3.57%
Quantization	2	3.57%
Autonomous Vehicles	2	3.57%
Real-Time Semantic Segmentation	2	3.57%
Deep Learning	2	3.57%
regression	2	3.57%
Medical Image Analysis	2	3.57%

Usage Over Time

This feature is experimental; we are continuously improving our matching algorithm.

Components

Component	Type	Add Remove
1x1 Convolution	Convolutions
Batch Normalization	Normalization
Convolution	Convolutions
Max Pooling	Pooling Operations
PReLU	Activation Functions
SpatialDropout	Regularization

Categories

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Image Model Blocks