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Related Concept Videos

Self-Schemas02:16

Self-Schemas

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In general, a schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Updated: May 23, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
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Reconstruction-Based Anomaly Localization via Knowledge-Informed Self-Training.

Cheng Qian, Xiaoxian Lao, Chunguang Li

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    This summary is machine-generated.

    This study introduces knowledge-informed self-training (KIST) for anomaly localization. KIST improves performance by using expert knowledge to better utilize weakly labeled anomalous samples in image reconstruction.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Anomaly localization is crucial in industrial settings.
    • Reconstruction-based methods are common due to simplicity and interpretability.
    • Current methods often underutilize available anomalous sample information.

    Purpose of the Study:

    • To improve anomaly localization performance by effectively using weakly labeled anomalous samples.
    • To integrate domain expert knowledge into reconstruction-based anomaly detection.
    • To develop a novel method that leverages both normal and anomalous data with expert insights.

    Main Methods:

    • Proposed a novel reconstruction-based method: knowledge-informed self-training (KIST).
    • Integrated expert knowledge into a self-training framework.
    • Utilized weakly labeled anomalous samples and expert knowledge to generate pixel-level pseudolabels.
    • Developed a novel loss function to guide reconstruction based on pseudolabels.

    Main Results:

    • Demonstrated the effectiveness of KIST across various datasets.
    • Showcased significant improvements in anomaly localization compared to existing methods.
    • Validated the advantage of incorporating expert knowledge and weakly labeled anomalous data.

    Conclusions:

    • KIST offers a superior approach to anomaly localization by effectively integrating expert knowledge.
    • The method successfully leverages weakly labeled anomalous data for enhanced performance.
    • This work advances reconstruction-based anomaly localization techniques.