Task Generalization

A Task Generalization is a machine learning capability that represents a model’s ability to perform well on new, unseen instances of a given task.

• AKA: Generalization Capability, Model Generalization.
• Context:
  • It can be measured using test data that the model has never seen during training.
  • It can be evaluated in zero-shot, few-shot, or transfer learning scenarios.
  • It can be influenced by the quality of training data and model architecture.
  • It can be enhanced by using diverse training data and advanced generalization techniques.
  • It can apply to various domains, such as image recognition, natural language processing, and time series forecasting.
  • ...
• Example(s):
  • Zero-Shot Learning Task, where a model generalizes to new tasks without any task-specific training.
  • Few-Shot Learning Task, where a model learns to perform tasks with minimal data.
  • Domain Adaptation Task, where a model generalizes to new data domains.
  • ...
• Counter-Example(s):
  • Benchmarking Task, which evaluates models rather than measuring their adaptability.
  • Specific Task, which focuses on a single predefined problem.
  • Multi-Task Learning, which trains a model on multiple related tasks rather than generalizing to new tasks.
• See: Machine Learning Task, Zero-Shot Learning, Few-Shot Learning, Transfer Learning.

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