Jove
Visualize
Contact Us
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 Concept Videos

Block Diagram Reduction01:22

Block Diagram Reduction

142
The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
The first step in this process is the identification and relocation of a branch point. A branch point, where a...
142
Neural Circuits01:25

Neural Circuits

944
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...
944
Chunking01:12

Chunking

38
Chunking is a powerful cognitive technique that improves short-term memory retention by organizing information into smaller, more manageable units. The brain, limited by working memory capacity, can more easily process and store information when it is divided into "chunks" rather than presented as discrete, unrelated elements. Chunking is especially useful when dealing with large amounts of information, such as numerical sequences, words, or complex ideas.
The principle behind chunking...
38
Storage01:23

Storage

57
A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze...
57
Deconvolution01:20

Deconvolution

117
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...
117
Neuroplasticity01:01

Neuroplasticity

252
Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.
252

You might also read

Related Articles

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

Sort by
Same author

A nuclear-encoded protein, mTERF6, mediates transcription termination of rpoA polycistron for plastid-encoded RNA polymerase-dependent chloroplast gene expression and chloroplast development.

Scientific reports·2018
Same author

Cumulative metabolic effects of low-dose benzo(<i>a</i>)pyrene exposure on human cells.

Toxicology research·2018
Same author

New MS network analysis pattern for the rapid identification of constituents from traditional Chinese medicine prescription Lishukang capsules in vitro and in vivo based on UHPLC/Q-TOF-MS.

Talanta·2018
Same author

Efficient Catalytic Performance for Acylation-Nazarov Cyclization Based on an Unusual Postsynthetic Oxidization Strategy in a Fe(II)-MOF.

Inorganic chemistry·2018
Same author

A prospective, mixed-methods, before and after study to identify the evidence base for the core components of an effective Paediatric Early Warning System and the development of an implementation package containing those core recommendations for use in the UK: Paediatric early warning system - utilisation and mortality avoidance- the PUMA study protocol.

BMC pediatrics·2018
Same author

The Polysaccharides from <i>Codonopsis pilosula</i> Modulates the Immunity and Intestinal Microbiota of Cyclophosphamide-Treated Immunosuppressed Mice.

Molecules (Basel, Switzerland)·2018

Related Experiment Video

Updated: May 16, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.2K

Counterclockwise block-by-block knowledge distillation for neural network compression.

Xiaowei Lan1, Yalin Zeng1, Xiaoxia Wei2

  • 1School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan, 250357, China.

Scientific Reports
|April 2, 2025
PubMed
Summary

This study introduces counterclockwise block-wise knowledge distillation (CBKD), a new method to improve knowledge distillation (KD) for model compression. CBKD enhances the transfer of intermediate knowledge between teacher and student models, boosting performance.

Keywords:
Deep neural networksKnowledge distillationModel compressionProgressive blockwise knowledge distillation

More Related Videos

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

331
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

8.9K

Related Experiment Videos

Last Updated: May 16, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.2K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

331
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

8.9K

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computer Vision

Background:

  • Model compression is vital for deploying large neural networks on resource-constrained devices.
  • Knowledge Distillation (KD) transfers knowledge from large teacher models to smaller student models.
  • Existing KD methods often use one or two stages, potentially limiting knowledge transfer.

Purpose of the Study:

  • To introduce a novel approach, counterclockwise block-wise knowledge distillation (CBKD), for optimizing the knowledge distillation process.
  • To mitigate the generation gap between teacher and student models during knowledge transfer.
  • To facilitate the transmission of intermediate-layer knowledge.

Main Methods:

  • CBKD divides teacher and student models into multiple sub-network blocks.
  • Knowledge is transferred from one teacher sub-block to the corresponding student sub-block per stage.
  • Deeper teacher sub-network blocks are assigned higher compression rates.

Main Results:

  • Experiments on tiny-imagenet-200 and CIFAR-10 datasets were conducted.
  • The proposed CBKD method demonstrated enhanced distillation performance.
  • CBKD improved various mainstream knowledge distillation approaches.

Conclusions:

  • CBKD offers an effective strategy for optimizing knowledge distillation.
  • The block-wise transfer and differential compression rates contribute to improved model compression.
  • This method enhances the efficiency and effectiveness of transferring knowledge in neural networks.