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Associative Learning01:27

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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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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.
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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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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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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...
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Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
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Decoding Natural Behavior from Neuroethological Embedding
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Generative Zero-Shot Learning via Low-Rank Embedded Semantic Dictionary.

Zhengming Ding, Ming Shao, Yun Fu

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |September 4, 2018
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    Summary
    This summary is machine-generated.

    This study introduces a novel two-stage generative adversarial network for zero-shot learning, enhancing visual recognition of unseen categories by bridging the semantic gap with low-rank embedding.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Zero-shot learning (ZSL) aims to recognize unseen categories by leveraging knowledge from seen categories.
    • A significant challenge in ZSL is the semantic gap between visual features and semantic representations, exacerbated by domain disparity.
    • Existing methods struggle to generalize effectively to unseen classes due to this domain disparity.

    Purpose of the Study:

    • To propose a novel framework for zero-shot learning that enhances the generalizability of semantic dictionaries.
    • To address the semantic gap and domain disparity in visual-semantic learning for unseen categories.
    • To improve the performance of visual recognition systems on identifying novel object categories.

    Main Methods:

    • A two-stage generative adversarial network (GAN) framework is designed for zero-shot learning.
    • The framework simultaneously learns a generative model and a semantic dictionary using low-rank embedding.
    • The first stage augments semantic features for unseen classes, while the second stage generates discriminant visual features to expand the seen feature space.

    Main Results:

    • The proposed method effectively augments semantic and visual features for unseen classes.
    • A more robust semantic dictionary is learned by utilizing the augmented data.
    • The approach successfully transfers visual characteristics from seen to unseen classes.
    • Experiments on four benchmarks show superior performance compared to state-of-the-art zero-shot learning algorithms.

    Conclusions:

    • The two-stage GAN framework effectively bridges the semantic gap in zero-shot learning.
    • Low-rank embedding enhances the generalizability of the semantic dictionary for unseen categories.
    • The proposed method offers a promising solution for improving visual recognition in scenarios with limited labeled data for new classes.