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Related Concept Videos

Associative Learning01:27

Associative Learning

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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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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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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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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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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
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Updated: Aug 24, 2025

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CLAST: Contrastive Learning for Arbitrary Style Transfer.

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    This study introduces contrastive learning for arbitrary style transfer, enabling automatic and robust modeling of content and style. The new framework enhances image stylization quality and flexibility.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Image Processing

    Background:

    • Arbitrary style transfer aims to apply the style of a reference image to a content image.
    • Current methods struggle with simultaneous content fidelity and style migration, lacking universality and robustness due to manual content/style definitions.

    Purpose of the Study:

    • To introduce contrastive learning for automatic and robust content and style modeling in arbitrary style transfer.
    • To develop a flexible and pluggable style transfer framework using novel contrastive losses.

    Main Methods:

    • Incorporated contrastive learning to automatically learn content and style representations.
    • Proposed instance-wise contrastive style losses and patch-wise contrastive content loss.
    • Integrated these losses with self-reconstruction strategies into a new style transfer framework.

    Main Results:

    • The proposed method demonstrates more robust and universal modeling of content and style compared to existing approaches.
    • The framework exhibits strong flexibility and can be applied to various style transfer modules.
    • Synthesized stylized images exhibit higher quality.

    Conclusions:

    • Contrastive learning offers a powerful approach for improving arbitrary style transfer by enabling automatic and robust feature modeling.
    • The developed framework provides a flexible and effective solution for high-quality image stylization.