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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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PresB-Net: parametric binarized neural network with learnable activations and shuffled grouped convolution.

Jungwoo Shin¹, HyunJin Kim¹

¹School of Electronics and Electrical Engineering, Dankook University, Yongin, South Korea.

Peerj. Computer Science

|February 3, 2022

Summary

This summary is machine-generated.

We introduce PresB-Net, a novel binarized neural network (BNN) that enhances performance by using learnable activations and a new normalization technique. This improved BNN model achieves superior classification accuracy on the CIFAR-100 dataset.

Keywords:

Binarized neural network Computer vision Convolutional neural network Machine learning Residual neural network

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Binarized Neural Networks (BNNs) offer computational efficiency and reduced hardware requirements.
Performance degradation remains a significant challenge for existing BNN models.

Purpose of the Study:

To develop a novel BNN model, PresB-Net, that overcomes performance limitations.
To enhance classification accuracy in BNNs through innovative architectural components and training strategies.

Main Methods:

PresB-Net integrates learnable activations with trainable parameters and shuffled grouped convolutions.
A novel normalization approach is proposed to mitigate imbalance in shuffled groups and stabilize gradient convergence.
The model incorporates these techniques to improve overall BNN performance.

Main Results:

The proposed PresB-Net model demonstrates enhanced classification performance compared to existing BNNs.
PresB-Net-18 achieved a Top-1 inference accuracy of 73.84% on the CIFAR-100 dataset, surpassing other BNN counterparts.

Conclusions:

PresB-Net represents a significant advancement in BNNs, effectively addressing performance degradation.
The novel normalization and learnable activation components contribute to improved accuracy and training stability in BNNs.