Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Oct 3, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Construction of Deep ReLU Nets for Spatially Sparse Learning.

Xia Liu, Di Wang, Shao-Bo Lin

IEEE Transactions on Neural Networks and Learning Systems

|February 14, 2022

Summary

We developed a new deep learning method to capture spatial sparseness, crucial for signal and image processing. This approach offers better accuracy and faster training than shallow learning models.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

The purine-rich element-binding protein ChPur-α negatively regulates Hsc70 transcription in Crassostrea hongkongensis.

Cell stress & chaperones·2017

Same author

Improved antitumor effect of ionizing radiation in combination with rapamycin for treating nasopharyngeal carcinoma.

Oncology letters·2017

Same author

Roles of Cells from the Arterial Vessel Wall in Atherosclerosis.

Mediators of inflammation·2017

Same author

Metabolic and microbial signatures in rat hepatocellular carcinoma treated with caffeic acid and chlorogenic acid.

Scientific reports·2017

Same author

Arsenic removal in aqueous solution by a novel Fe-Mn modified biochar composite: Characterization and mechanism.

Ecotoxicology and environmental safety·2017

Same author

Antidiabetic activities of polysaccharides separated from Inonotus obliquus via the modulation of oxidative stress in mice with streptozotocin-induced diabetes.

PloS one·2017

Area of Science:

Machine Learning
Signal Processing
Image Processing

Background:

Interpretable deep networks are crucial but challenging to train.
Spatial sparseness is an important feature in signal and image processing.

Purpose of the Study:

To develop a constructive approach for generating deep networks that capture spatial sparseness.
To theoretically and numerically validate the effectiveness of this approach.

Main Methods:

A constructive approach to generate deep neural networks.
Theoretical analysis to prove generalization error bounds.
Numerical verification comparing the approach with shallow learning.

Main Results:

The constructive approach yields deep network estimates with optimal generalization error bounds.

More Related Videos

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Related Experiment Videos

Last Updated: Oct 3, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

The proposed deep learning method significantly outperforms shallow learning.

Achieves better prediction accuracy with reduced training time.

Conclusions:

The constructive approach effectively generates interpretable deep networks with spatial sparseness.
This method offers theoretical guarantees and practical advantages over shallow learning.
It represents a significant advancement in machine learning for signal and image processing.