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

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

139
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
139
Associative Learning01:27

Associative Learning

507
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...
507
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

727
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...
727
Introduction to Learning01:18

Introduction to Learning

506
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...
506
Survival Tree01:19

Survival Tree

132
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
132
Observational Learning01:12

Observational Learning

263
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...
263

You might also read

Related Articles

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

Sort by
Same author

Mechanistic insights into nitrogen loss during food waste composting revealed by metagenomic and qPCR analyses under varying substrate C/N ratios.

Bioresource technology·2026
Same author

Defect-engineering induced charge transfer enhanced reactivity of ultralight boron nitride aerogel for iodine adsorption: A multiscenario effective capture study.

Journal of hazardous materials·2026
Same author

A general framework for vectorial detection PSF analysis for oblique plane microscopy utilizing trans-medium intermediate image coupling.

Micron (Oxford, England : 1993)·2026
Same author

A computer-aided method for the automatic localization of the left ventricular long axis on coronary computed tomography angiography.

Quantitative imaging in medicine and surgery·2026
Same author

Prostaglandin I2 Receptor Activation Promotes Alveolar Regeneration via the JUN/p53 Pathway.

American journal of respiratory and critical care medicine·2026
Same author

Oral cascade-responsive nanomedicine to disrupt macrophage-neutrophil crosstalk for intestinal barrier repair.

Biomaterials·2026
Same journal

Relation DETR+: Exploring Explicit Position Relation Prior for Dense Prediction.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

CAFE: Cross-View Adaptive Fusion and Cluster Center Enhancement for Robust Multi-View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Learning Shape Anchors for Holistic Indoor Scene Understanding.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles

Related Experiment Video

Updated: Aug 19, 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

605

GH-Feat: Learning Versatile Generative Hierarchical Features From GANs.

Yinghao Xu, Yujun Shen, Jiapeng Zhu

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |December 1, 2022
    PubMed
    Summary
    This summary is machine-generated.

    Generative Hierarchical Features (GH-Feat) from GANs show strong potential for various computer vision tasks. These features, learned during image synthesis, offer versatile transferability for both generative and discriminative applications.

    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

    663
    Generating Strictly Controlled Stimuli for Figure Recognition Experiments
    05:39

    Generating Strictly Controlled Stimuli for Figure Recognition Experiments

    Published on: March 18, 2019

    5.3K

    Related Experiment Videos

    Last Updated: Aug 19, 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

    605
    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

    663
    Generating Strictly Controlled Stimuli for Figure Recognition Experiments
    05:39

    Generating Strictly Controlled Stimuli for Figure Recognition Experiments

    Published on: March 18, 2019

    5.3K

    Area of Science:

    • Computer Vision
    • Generative Adversarial Networks (GANs)
    • Machine Learning

    Background:

    • Generative Adversarial Networks (GANs) excel at synthesizing realistic images, learning data distributions and emergent hierarchical visual features.
    • These learned features possess potential for diverse computer vision applications beyond image generation.

    Purpose of the Study:

    • To investigate the potential of generative features learned from GANs for various computer vision tasks.
    • To develop and evaluate Generative Hierarchical Features (GH-Feat) for both generative and discriminative applications.

    Main Methods:

    • Trained an encoder using a pre-trained StyleGAN generator as a learned loss function.
    • Extracted Generative Hierarchical Features (GH-Feat) that align with layer-wise GAN representations.
    • Applied spatial expansion to GH-Feat for fine-grained semantic segmentation.

    Main Results:

    • GH-Feat demonstrated versatile transferability across image editing, processing, harmonization, face verification, landmark detection, layout prediction, and image retrieval.
    • GH-Feat facilitated fine-grained semantic segmentation with limited annotations.
    • Both qualitative and quantitative results showed appealing performance.

    Conclusions:

    • Generative features learned by GANs are highly effective for a broad spectrum of computer vision tasks.
    • GH-Feat offers a powerful and transferable feature representation for computer vision.
    • The developed GH-Feat shows promise for advancing tasks requiring detailed visual understanding and segmentation.