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

Mean Absolute Deviation01:13

Mean Absolute Deviation

2.7K
The mean absolute deviation is also a measure of the variability of data in a sample. It is the absolute value of the average difference between the data values and the mean.
Let us consider a dataset containing the number of unsold cupcakes in five shops: 10, 15, 8, 7, and 10. Initially, calculate the sample mean. Then calculate the deviation, or the difference, between each data value and the mean. Next, the absolute values of these deviations are added and divided by the sample size to...
2.7K
Divergence and Stokes' Theorems01:06

Divergence and Stokes' Theorems

1.9K
The divergence and Stokes' theorems are a variation of Green's theorem in a higher dimension. They are also a generalization of the fundamental theorem of calculus. The divergence theorem and Stokes' theorem are in a way similar to each other; The divergence theorem relates to the dot product of a vector, while Stokes' theorem relates to the curl of a vector. Many applications in physics and engineering make use of the divergence and Stokes' theorems, enabling us to write...
1.9K
Improving Translational Accuracy02:07

Improving Translational Accuracy

11.8K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
11.8K
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

1.7K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
1.7K
Dot Product: Problem Solving01:21

Dot Product: Problem Solving

430
The dot product is a powerful tool in problem-solving involving vectors, given that the dot product of two vectors is the product of their magnitudes and the cosine of the angle between them measured anti-clockwise. Solving problems involving the dot product requires understanding its properties and developing a step-by-step process to solve them. Here are the main steps to follow when solving any general problem involving the dot product:
Identify the problem: Start by reading the problem and...
430
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

706
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
706

You might also read

Related Articles

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

Sort by
Same author

[Scapular belt for the treatment of comminuted fractures of scapula].

Zhongguo gu shang = China journal of orthopaedics and traumatology·2010
Same author

Manipulation of ordered nanostructures of protonated polyoxometalate through covalently bonded modification.

Chemistry (Weinheim an der Bergstrasse, Germany)·2010
Same author

Developments in nonsteroidal antiandrogens targeting the androgen receptor.

ChemMedChem·2010
Same author

Dynamic presentation of immobilized ligands regulated through biomolecular recognition.

Journal of the American Chemical Society·2010
Same author

[Research on crop-weed discrimination using a field imaging spectrometer].

Guang pu xue yu guang pu fen xi = Guang pu·2010
Same author

A palladium/copper bimetallic catalytic system: dramatic improvement for Suzuki-Miyaura-type direct C-H arylation of azoles with arylboronic acids.

Chemistry (Weinheim an der Bergstrasse, Germany)·2010

Related Experiment Video

Updated: Sep 9, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.2K

Adaptive metric for knowledge distillation by deep Bregman divergence.

Tongtong Yuan1, Zixuan Xu2, Bo Liu1

  • 1Beijing University of Technology, China.

Neural Networks : the Official Journal of the International Neural Network Society
|September 4, 2025
PubMed
Summary

This study introduces a novel deep Bregman divergence metric for knowledge distillation (KD). This adaptive approach improves the transfer of knowledge between neural networks, outperforming existing methods in model compression and performance.

Keywords:
Bregman divergenceImage classificationKnowledge distillationMetric learning

More Related Videos

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

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.3K

Related Experiment Videos

Last Updated: Sep 9, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.2K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.3K

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computer Vision

Background:

  • Knowledge distillation (KD) enables deploying accurate, large models on resource-constrained devices by training smaller, lightweight models.
  • Existing KD methods often struggle with effectively transferring knowledge from teacher to student networks, particularly concerning middle-layer representations due to structural and distributional variations.
  • Traditional metrics for comparing feature representations lack adaptability to the heterogeneous characteristics of deep neural networks.

Purpose of the Study:

  • To develop a more effective and robust knowledge distillation method by addressing limitations of traditional feature comparison metrics.
  • To propose a parameterized and adaptive metric for knowledge transfer that accounts for variations in feature distributions.
  • To enhance existing knowledge distillation techniques, especially those focused on probability outputs.

Main Methods:

  • Introduction of a parameterized and adaptive metric based on deep Bregman divergence for knowledge distillation.
  • The proposed divergence function is learned from data, allowing it to adapt to underlying feature distributions across different layers and models.
  • The method is designed to complement existing KD techniques, functioning as an enhancement (x+Bregman) for probability output distillation.

Main Results:

  • Extensive experiments demonstrate that the proposed deep Bregman divergence method significantly outperforms existing knowledge distillation approaches.
  • The approach achieves superior performance across diverse datasets and various network architectures, validating its effectiveness and robustness.
  • The adaptive metric successfully captures spatial, semantic, and statistical variations between teacher and student network features.

Conclusions:

  • The proposed deep Bregman divergence metric offers a more effective and robust solution for knowledge distillation compared to traditional methods.
  • This adaptive approach facilitates better knowledge transfer, leading to improved performance in lightweight models.
  • The method's compatibility with other KD techniques highlights its versatility and potential for broad application in model compression.