Jove
Visualize
Contact Us

Related Concept Videos

Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

541
In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
541
Convolution Properties II01:17

Convolution Properties II

350
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
350
Convolution Properties I01:20

Convolution Properties I

318
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
318
Deconvolution01:20

Deconvolution

358
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
358
Vision01:24

Vision

57.5K
Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
57.5K
Neural Circuits01:25

Neural Circuits

2.0K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
2.0K

You might also read

Related Articles

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

Sort by
Same author

Routine application of three-dimensional transesophageal echocardiography-based device selection for transcatheter closure of atrial septal defect without balloon sizing.

European heart journal. Cardiovascular Imaging·2026
Same author

Convolutional neural network-based classification of craniosynostosis and suture lines from multi-view cranial X-rays.

Scientific reports·2024
Same author

Variational Deep Image Restoration.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2022
Same author

Co-Salient Object Detection Based on Deep Saliency Networks and Seed Propagation Over an Integrated Graph.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2018
Same author

Does Sacral Slanting Affect Distal Adding-on in Lenke Type 1A Adolescent Idiopathic Scoliosis?

Spine·2018
Same author

Referral patterns and patient characteristics at the first visit to a scoliosis center: a 2-year experience in South Korea without a school scoliosis-screening program.

Journal of neurosurgery. Pediatrics·2018
Same journal

Granular Ball-Based Noise-Resistant Fuzzy Multineighborhood Feature Selection via Label Enhancement and Feature Graph.

IEEE transactions on neural networks and learning systems·2026
Same journal

Fighting Evolving Spam With ARTMAP Models: A Noise-Resilient Online Detection Framework.

IEEE transactions on neural networks and learning systems·2026
Same journal

HyperSAT: Unsupervised Hypergraph Neural Networks for Weighted MaxSAT Problems.

IEEE transactions on neural networks and learning systems·2026
Same journal

Negation of Basic Belief Assignment in Multisource Information Fusion on Dempster-Shafer Theory With Applications in Pattern Classification.

IEEE transactions on neural networks and learning systems·2026
Same journal

Intervention Feasible Region and Driver Risk Capacity Aware Human-Machine Collaborative Safe Trajectory Planning.

IEEE transactions on neural networks and learning systems·2026
Same journal

A Unified Differential Denoising Learning Framework With a Pre-Trained Model and Fuzzy Graph Networks for Drug-Drug Interaction Prediction.

IEEE transactions on neural networks and learning systems·2026
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Nov 1, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

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

Published on: December 15, 2023

730

Inverse-Based Approach to Explaining and Visualizing Convolutional Neural Networks.

Hyuk Jin Kwon, Hyung Il Koo, Jae Woong Soh

    IEEE Transactions on Neural Networks and Learning Systems
    |June 17, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an inverse-based method to understand and visualize convolutional neural networks (CNNs), applicable to both classification and regression tasks. The novel approach enhances interpretability by identifying key activations and revealing potential performance issues in image super-resolution.

    Related Experiment Videos

    Last Updated: Nov 1, 2025

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

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

    Published on: December 15, 2023

    730

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Existing methods for analyzing Convolutional Neural Networks (CNNs) primarily focus on global scores and pixelwise input contributions.
    • Analysis of CNNs for multi-labeled outputs or regression tasks remains underexplored, despite their success in image classification.

    Purpose of the Study:

    • To propose a novel inverse-based approach for understanding and visualizing CNNs.
    • To extend CNN analysis to regression and multi-label classification tasks.
    • To improve the interpretability and diagnostic capabilities of CNN models.

    Main Methods:

    • Developed a layerwise inverse procedure by computing the inverse of a feedforward pass.
    • Incorporated two key observations: consistency of internal activations and desirability of minimal activation for interpretability.
    • Applied the method to both image classification and regression tasks, including single image super-resolution.

    Main Results:

    • The proposed method enables unified analysis of CNNs for classification and regression.
    • Attribution analysis for image classification achieved performance comparable to state-of-the-art methods.
    • A novel visualization plot was developed to illustrate the trade-off between activation levels and class re-identification rates.
    • Analysis of super-resolution CNNs revealed overlooked frequency bands potentially degrading performance.

    Conclusions:

    • The inverse-based approach provides a versatile framework for CNN interpretability across different tasks.
    • The method offers insights into model behavior, aiding in performance diagnostics and improvement.
    • Future work can leverage this framework for more sophisticated CNN analysis and development.