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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.
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The Ideal Transformer01:26

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In single-phase two-winding transformers, two windings are coiled around a magnetic core characterized by cross-sectional area A and magnetic permeability μ. A phasor current i1 enters the left winding while i2 exits the right winding, establishing the fundamental working of the transformer through electromagnetic principles.
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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...
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In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
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ZS-VAT: Learning Unbiased Attribute Knowledge for Zero-Shot Recognition Through Visual Attribute Transformer.

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    Summary

    This study introduces a new visual attribute Transformer for zero-shot learning (ZSL) to address biased attribute knowledge. The ZS-VAT model effectively learns unbiased attribute knowledge, improving zero-shot recognition performance.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Zero-shot learning (ZSL) relies on attribute knowledge for knowledge transfer.
    • Existing ZSL methods often learn biased attribute knowledge, hindering recognition performance.

    Purpose of the Study:

    • To propose a novel visual attribute Transformer for zero-shot recognition (ZS-VAT).
    • To learn unbiased attribute knowledge and improve ZSL performance.

    Main Methods:

    • Developed an attribute-head self-attention (AHSA) mechanism to learn unbiased attribute knowledge.
    • Introduced an attribute fusion model (AFM) to recover category knowledge from attribute knowledge.
    • Combined attribute embedding prediction (AEP) and global embedding prediction (GEP) for final semantic prediction.

    Main Results:

    • ZS-VAT effectively learns unbiased attribute knowledge, reducing mutual influence between attributes.
    • AHSA and AFM demonstrated synergistic knowledge enhancement.
    • The proposed scheme achieved state-of-the-art results on two benchmark datasets for generalized ZSL (GZSL).

    Conclusions:

    • ZS-VAT offers an effective and interpretable solution for learning unbiased attribute knowledge in ZSL.
    • The proposed method significantly improves zero-shot recognition performance compared to existing GZSL methods.