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Related Concept Videos

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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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Cognitive learning is based on purposive behavior, incidental learning, and insight 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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Is Class-Incremental Enough for Continual Learning?

Andrea Cossu1,2, Gabriele Graffieti3, Lorenzo Pellegrini3

  • 1Pervasive AI Lab, Computer Science Department, University of Pisa, Pisa, Italy.

Frontiers in Artificial Intelligence
|April 11, 2022
PubMed
Summary
This summary is machine-generated.

This study argues that current continual learning research overly focuses on class-incremental scenarios. Introducing repetition in learning environments better reflects real-world data and aids model assessment.

Keywords:
catastrophic forgettingclass-incrementalclass-incremental with repetitioncontinual learninglifelong learning

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computer Science

Background:

  • Continual learning models aim to learn sequentially from data streams.
  • Current research predominantly uses class-incremental scenarios, where past classes are not revisited.
  • This focus may artificially inflate catastrophic forgetting and neglect other learning objectives.

Purpose of the Study:

  • To challenge the over-reliance on class-incremental scenarios in continual learning research.
  • To advocate for the adoption of continual learning scenarios that incorporate data repetition.
  • To highlight the limitations of current experimental settings for comprehensive model evaluation.

Main Methods:

  • Analysis of existing continual learning literature and experimental trends.
  • Conceptualization and description of 'class-incremental with repetition' scenarios.
  • Argumentation for the benefits of incorporating repetition in learning environments.

Main Results:

  • Class-incremental scenarios exacerbate catastrophic forgetting, potentially hindering progress.
  • Real-world learning often involves natural repetition of concepts.
  • Repetition can mitigate catastrophic forgetting and improve forward transfer.

Conclusions:

  • A shift towards continual learning scenarios with built-in repetition is necessary.
  • These scenarios offer a more realistic and comprehensive evaluation of model capabilities.
  • Further research into 'class-incremental with repetition' settings is encouraged.