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

Observational Learning01:12

Observational Learning

697
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
697
Introduction to Learning01:18

Introduction to Learning

751
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
751
Neural Circuits01:25

Neural Circuits

2.4K
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...
2.4K
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

1.2K
Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
1.2K
Associative Learning01:27

Associative Learning

948
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
948

You might also read

Related Articles

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

Sort by
Same author

A Vision-based System for Monitoring Eating Behaviors and Musculoskeletal Function.

Journal of healthcare informatics researchยท2026
Same author

Stain-free artificial intelligence-assisted light microscopy for the identification of leukocyte morphology change in presence of bacteria.

Frontiers in bioinformaticsยท2026
Same author

Handwritten Text Recognition: A Survey.

IEEE transactions on pattern analysis and machine intelligenceยท2025
Same author

Stain-free artificial intelligence-assisted light microscopy for the identification of blood cells in microfluidic flow.

Frontiers in bioinformaticsยท2025
Same author

Towards Explainable Graph Embeddings for Gait Assessment Using Per-Cluster Dimensional Weighting.

Sensors (Basel, Switzerland)ยท2025
Same author

A lightweight approach to gait abnormality detection for At Home health monitoring.

Computers in biology and medicineยท2025

Related Experiment Video

Updated: Dec 6, 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

871

Incremental Unsupervised Domain-Adversarial Training of Neural Networks.

Antonio-Javier Gallego, Jorge Calvo-Zaragoza, Robert B Fisher

    IEEE Transactions on Neural Networks and Learning Systems
    |October 7, 2020
    PubMed
    Summary

    This study introduces an incremental domain adaptation (DA) method for machine learning models. By iteratively self-labeling target data, it improves model performance on new data distributions.

    More Related Videos

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
    03:14

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

    Published on: December 6, 2024

    882

    Related Experiment Videos

    Last Updated: Dec 6, 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

    871
    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
    03:14

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

    Published on: December 6, 2024

    882

    Area of Science:

    • Machine Learning
    • Computer Science
    • Statistical Learning

    Background:

    • Supervised learning assumes training and test data share distributions.
    • Distribution shifts cause unpredictable model behavior.
    • Domain Adaptation (DA) addresses this challenge, especially for deep neural networks.

    Purpose of the Study:

    • To develop an incremental approach for domain adaptation.
    • To improve model performance when training and test data distributions differ.
    • To enhance existing unsupervised DA algorithms.

    Main Methods:

    • An iterative, incremental domain adaptation strategy is proposed.
    • Leverages an unsupervised DA algorithm to identify confident target samples.
    • Employs self-labeling to add selected target samples to the source training set.
    • Utilizes adversarial training principles by shifting samples between domains.

    Main Results:

    • The incremental approach demonstrates clear performance improvements over non-incremental methods.
    • Outperforms several state-of-the-art domain adaptation algorithms.
    • Effective across multiple datasets, showcasing robustness.

    Conclusions:

    • Incremental domain adaptation is a promising direction for handling distribution shifts.
    • The proposed self-labeling and adversarial strategy enhances DA effectiveness.
    • This method offers a robust alternative to existing DA techniques.