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Related Concept Videos

Reinforcement Schedules01:24

Reinforcement Schedules

288
Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
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Reinforcement01:23

Reinforcement

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Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
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Purposive Learning01:22

Purposive 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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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.
Classical conditioning, also known...
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Observational Learning01:12

Observational 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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Operant Conditioning01:21

Operant Conditioning

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Operant conditioning, a key concept in behavioral psychology, involves using reinforcement and punishment to alter the likelihood of a behavior being repeated. B.F. introduced this type of conditioning. Skinner focused on voluntary behaviors and the consequences that follow them, influencing whether these behaviors will be strengthened or diminished.
Reinforcement in operant conditioning can be positive or negative, both of which serve to increase the likelihood of a behavior. Positive...
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Task-Driven Semantic Coding via Reinforcement Learning.

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    |July 2, 2021
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    Summary
    This summary is machine-generated.

    This study introduces a novel approach for task-driven semantic video coding using reinforcement learning (RL) to optimize bit allocation. The method achieves significant bitrate savings while preserving essential semantic information for various intelligent media tasks.

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    Area of Science:

    • Computer Vision
    • Multimedia Signal Processing
    • Artificial Intelligence

    Background:

    • Intelligent media applications require task-driven semantic coding to preserve crucial information in videos and images.
    • Deep neural network (DNN) codecs offer end-to-end optimization but traditional hybrid coding frameworks lack this integration for semantic fidelity.
    • Integrating task-driven semantic fidelity metrics into traditional hybrid coding remains a significant challenge.

    Purpose of the Study:

    • To develop a method for task-driven semantic coding within the widely used traditional hybrid coding framework.
    • To enable end-to-end optimization of semantic fidelity by addressing the limitations of current hybrid coding structures.
    • To improve the efficiency of semantic video/image coding for applications like detection and medical diagnosis.

    Main Methods:

    • Designed task-specific semantic maps to extract pixelwise semantic fidelity from videos/images.
    • Implemented task-driven semantic coding by employing reinforcement learning (RL) for semantic bit allocation.
    • Formulated the semantic bit allocation as a Markov decision process (MDP) solved by an RL agent that determines quantization parameters (QPs) for coding units (CUs).

    Main Results:

    • Achieved significant bitrate savings, ranging from 34.39% to 52.62%, compared to High Efficiency Video Coding (H.265/HEVC).
    • Demonstrated superior performance across various tasks including classification, detection, and segmentation.
    • Maintained equivalent task-related semantic fidelity at reduced bitrates.

    Conclusions:

    • The proposed RL-based semantic bit allocation effectively integrates task-driven semantic fidelity into traditional hybrid video coding frameworks.
    • This approach offers a practical and efficient solution for semantic coding in intelligent media applications.
    • Significant bitrate reductions achieved highlight the potential for widespread industry adoption.