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

Stereotypes, Prejudice, and Discrimination02:55

Stereotypes, Prejudice, and Discrimination

95.4K
Humans are very diverse and although we share many similarities, we also have many differences. The social groups we belong to help form our identities (Tajfel, 1974). These differences may be difficult for some people to reconcile, which may lead to prejudice toward people who are different. Prejudice is a negative attitude and feeling toward an individual based solely on one’s membership in a particular social group (Allport, 1954; Brown, 2010). Prejudice is common against people who...
95.4K
Restorative Care01:19

Restorative Care

2.4K
Restorative care is provided once a patient has been discharged from a healthcare facility and requires additional services. The additional services include home care, rehabilitation programs, and extended care. Restorative care centers help the patient regain their previous level of functioning or acquire a new level of functioning due to the incapacitating effects of a disease or a disability. It aims to assist patients in enhancing their quality of life by encouraging independence,...
2.4K
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

1.4K
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...
1.4K
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.6K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
2.6K
Associative Learning01:27

Associative Learning

1.3K
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...
1.3K
Purposive Learning01:22

Purposive Learning

513
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
513

You might also read

Related Articles

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

Sort by
Same author

A critical perspective on finite sample conformal prediction theory in medical applications.

Artificial intelligence in medicine·2026
Same author

Seeing through fibers: unsupervised image reconstruction in fiber bundle imaging systems.

Optics express·2026
Same author

Imagining and building wise machines: the centrality of AI metacognition.

Trends in cognitive sciences·2026
Same author

Latent Causal Diffusions for Single-Cell Perturbation Modeling.

ArXiv·2026
Same author

In silico biological discovery with large perturbation models.

Nature computational science·2025
Same author

Beating spectral bandwidth limits for large aperture broadband nano-optics.

Nature communications·2025

Related Experiment Video

Updated: Feb 8, 2026

An Objective and Reproducible Test of Olfactory Learning and Discrimination in Mice
09:33

An Objective and Reproducible Test of Olfactory Learning and Discrimination in Mice

Published on: March 22, 2018

9.2K

Discriminative Transfer Learning for General Image Restoration.

Lei Xiao, Felix Heide, Wolfgang Heidrich

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |July 12, 2018
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new discriminative transfer learning method for general image restoration. It enables single-pass training for diverse tasks and conditions, offering efficient and reusable image restoration models.

    More Related Videos

    High Resolution Quantitative Synaptic Proteome Profiling of Mouse Brain Regions After Auditory Discrimination Learning
    10:36

    High Resolution Quantitative Synaptic Proteome Profiling of Mouse Brain Regions After Auditory Discrimination Learning

    Published on: December 15, 2016

    11.0K
    Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques
    09:48

    Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

    Published on: June 30, 2017

    7.9K

    Related Experiment Videos

    Last Updated: Feb 8, 2026

    An Objective and Reproducible Test of Olfactory Learning and Discrimination in Mice
    09:33

    An Objective and Reproducible Test of Olfactory Learning and Discrimination in Mice

    Published on: March 22, 2018

    9.2K
    High Resolution Quantitative Synaptic Proteome Profiling of Mouse Brain Regions After Auditory Discrimination Learning
    10:36

    High Resolution Quantitative Synaptic Proteome Profiling of Mouse Brain Regions After Auditory Discrimination Learning

    Published on: December 15, 2016

    11.0K
    Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques
    09:48

    Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

    Published on: June 30, 2017

    7.9K

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Image Processing

    Background:

    • Discriminative learning methods offer efficient image restoration but require task-specific training.
    • Current approaches are time-consuming and difficult to scale across various restoration problems and conditions.

    Purpose of the Study:

    • To develop a general image restoration method using discriminative transfer learning.
    • To enable efficient, single-pass training applicable to multiple tasks and conditions.
    • To create a reusable model for diverse image restoration challenges.

    Main Methods:

    • Incorporating formal proximal optimization with discriminative learning.
    • Developing a single-pass discriminative training framework.
    • Enabling model transferability to new likelihood terms and existing priors.

    Main Results:

    • Achieved efficiency comparable to previous discriminative approaches.
    • Demonstrated reusability across various image restoration problems and conditions.
    • Showcased the ability to transfer trained models to untrained tasks.

    Conclusions:

    • The proposed method provides a versatile and efficient solution for general image restoration.
    • Single-pass training significantly reduces complexity and time requirements.
    • The model's transferability allows for broad applicability and improved restoration quality.