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Related Experiment Video

Updated: Nov 11, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

Uncorrelated feature encoding for faster image style transfer.

Minseong Kim¹, Hyun-Chul Choi²

¹Alchera Inc., 225-15 Pangyoyeok-Ro, Bundang-gu Seongnam, Gyeonggi-do 13494, Republic of Korea.

Neural Networks : the Official Journal of the International Neural Network Society

|March 25, 2021

Summary

This study introduces an end-to-end learning method for image style transfer, optimizing both encoder and decoder networks. It significantly speeds up processing by reducing feature map correlations, enabling faster, high-quality style transfer.

Keywords:

Convolutional neural networks End-to-end learning Image style transfer Redundant channel elimination Uncorrelated feature encoding Uncorrelation loss

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Area of Science:

Computer Vision
Deep Learning
Artificial Intelligence

Background:

Current image style transfer methods rely on pre-trained networks not optimized for the task.
Existing methods require complex and time-consuming feature alignment due to channel correlations.

Purpose of the Study:

To propose an end-to-end learning approach for image style transfer that optimizes encoder and decoder networks simultaneously.
To reduce the computational complexity associated with feature alignment in style transfer.

Main Methods:

Implemented end-to-end training, updating both encoder and decoder parameters for style transfer.
Introduced an "uncorrelation loss" based on the total correlation coefficient among encoder channel responses.
Utilized a lightweight transformer for correlation-unaware feature alignment.

Main Results:

Achieved faster forward processing speeds due to reduced feature map correlations.
Significantly reduced channel redundancy in encoded features during training.
Demonstrated the possibility of channel elimination with minimal impact on style quality.

Conclusions:

The proposed method offers an efficient and effective approach to image style transfer.
The technique allows for user control over style strength and supports multi-scale style transfer.
Optimizing encoder-decoder networks and reducing feature correlation are key to improving style transfer performance.