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Related Experiment Videos

Phase Transitions in Transfer Learning for High-Dimensional Perceptrons.

Oussama Dhifallah1, Yue M Lu1

  • 1John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA.

Entropy (Basel, Switzerland)
|April 3, 2021
PubMed
Summary
This summary is machine-generated.

Transfer learning improves generalization by leveraging source task knowledge. This study theoretically shows that task similarity determines if transfer learning is beneficial, preventing negative transfer past a specific threshold.

Keywords:
phase transitionsstatisticstransfer learning

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Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Theoretical Computer Science

Background:

  • Transfer learning aims to enhance target task performance using knowledge from related source tasks.
  • A key challenge is understanding when transfer learning is beneficial and avoiding negative transfer, which occurs when tasks are too dissimilar.

Purpose of the Study:

  • To provide a theoretical analysis of transfer learning.
  • To investigate the conditions under which transfer learning transitions from negative to positive impact.

Main Methods:

  • The study analyzes a pair of related perceptron learning tasks.
  • Asymptotic analysis is employed to examine the behavior of transfer learning.

Main Results:

  • The theoretical model reproduces key phenomena observed in practical transfer learning scenarios.
  • A phase transition is identified, demonstrating a shift from negative to positive transfer as task similarity increases.

Conclusions:

  • Task similarity is a critical factor in the success of transfer learning.
  • A well-defined threshold of task similarity exists, beyond which positive transfer is observed, mitigating negative transfer effects.