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

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

165
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
165
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

192
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...
192
Introduction to Learning01:18

Introduction to Learning

591
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...
591
Associative Learning01:27

Associative Learning

640
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...
640
Observational Learning01:12

Observational Learning

360
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...
360
Neural Circuits01:25

Neural Circuits

1.8K
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...
1.8K

You might also read

Related Articles

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

Sort by
Same author

Memristive neuromorphic circuit design inspired by the neural mechanisms of conditioned fear.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Vapor-phase Annealing toward Homogeneous Bottom-Up Crystallization in Perovskite Solar Cells.

ACS applied materials & interfaces·2026
Same author

ResSAT: enhancing spatial transcriptomics prediction from H&E-stained histology images with an interactive spot transformer.

Genome biology·2026
Same author

Multi-imidazolium cage-like microspheres: Synergistic multi site hydrogen bonding and geometric confinement for selective <sup>99</sup>TcO<sub>4</sub><sup>-</sup>/ReO<sub>4</sub><sup>-</sup> capture in harsh media.

Journal of colloid and interface science·2026
Same author

Pore size engineering in covalent organic frameworks for high-performance anion exchange membranes.

Nanoscale·2026
Same author

DEDSAC: Centralized microgrid dispatch via dual exploration mechanism enhanced diffusion soft actor-critic.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

ASSR-Net: Anisotropic Structure-Aware and Spectrally Recalibrated Network for Hyperspectral Image Fusion.

IEEE transactions on neural networks and learning systems·2026
Same journal

PIMPC-GNN: Physics-Informed Multiphase Consensus Learning for Enhancing Imbalanced Node Classification in Graph Neural Networks.

IEEE transactions on neural networks and learning systems·2026
Same journal

Quantum Rényi α-Entropies for Graph Characterization.

IEEE transactions on neural networks and learning systems·2026
Same journal

LANet: A Lightweight and Accurate Balanced Network Based on State Space Models for Real-Time Semantic Segmentation.

IEEE transactions on neural networks and learning systems·2026
Same journal

MENDNet: Memory-Enhanced Dependency Network for Multistock Movement Prediction.

IEEE transactions on neural networks and learning systems·2026
Same journal

Temporal Mask-Embedding Learning and Query-Refined Head Network for Visual Tracking.

IEEE transactions on neural networks and learning systems·2026
See all related articles

Related Experiment Videos

Generative Mixup Networks for Zero-Shot Learning.

Bingrong Xu, Zhigang Zeng, Cheng Lian

    IEEE Transactions on Neural Networks and Learning Systems
    |January 28, 2022
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces generative mixup networks with semantic graph alignment to improve generalized zero-shot learning. The novel approach addresses imbalanced data and irregular class structures for better unseen class recognition.

    Related Experiment Videos

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Zero-shot learning (ZSL) enables recognition of unseen classes by transferring knowledge from seen classes through a shared semantic space.
    • Generalized zero-shot learning (GZSL) faces challenges due to imbalanced data distributions between seen and unseen classes and irregular class structures.
    • Existing ZSL methods often struggle with satisfactory performance in GZSL tasks, particularly when dealing with complex data distributions and attribute mappings.

    Purpose of the Study:

    • To propose a novel generative mixup network with semantic graph alignment to address limitations in generalized zero-shot learning.
    • To improve the handling of imbalanced data distributions and irregular class structures in visual feature to semantic attribute mapping.
    • To enhance the recognition accuracy for unseen classes in generalized zero-shot learning scenarios.

    Main Methods:

    • Synthesizing class-conditional samples using semantic information to reconstruct class-based feature distributions.
    • Employing a mixup mechanism for data augmentation to enhance model generalization capabilities.
    • Developing a triplet gradient matching loss for latent space continuity and improved sample discrimination.
    • Utilizing a semantic attribute similarity graph to guide feature generation and capture intrinsic correlations.

    Main Results:

    • The proposed model demonstrates superior performance compared to state-of-the-art methods on several generalized zero-shot learning benchmarks.
    • The generative approach effectively recovers class-based feature distributions and mitigates issues from imbalanced data.
    • Semantic graph alignment aids in capturing intrinsic correlations, guiding feature generation for improved accuracy.

    Conclusions:

    • The novel generative mixup networks with semantic graph alignment offer a robust solution for generalized zero-shot learning.
    • The method effectively addresses data imbalance and irregular class structures, leading to significant performance gains.
    • This approach advances the capability of zero-shot learning in recognizing unseen classes within complex datasets.