Lifelong learning with Shared and Private Latent Representations learned through synaptic intelligence.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Deep Learning

Background:

• Lifelong learning aims to enable models to continuously learn from a sequence of tasks without forgetting previous knowledge.
• Existing methods often struggle to balance retaining old knowledge and acquiring new information efficiently.
• Parameter efficiency and reduced training time are critical challenges in lifelong learning scenarios.

Purpose of the Study:

• To propose a novel lifelong learning method named Shared and Private Latent Representations (SPLR).
• To enable models to learn both task-invariant representations and task-specific features.
• To improve parameter efficiency and reduce training time in lifelong learning.

Main Methods:

• SPLR learns representations through synaptic intelligence, considering the entire parameter learning trajectory.
• It distinguishes between task-invariant structures (shared across tasks) and private properties (task-specific).
• L1 regularization is applied to promote sparsity in network weights, reducing parameter quantity.

Main Results:

• SPLR achieves comparable performance to existing lifelong learning approaches on multiple datasets.
• The method successfully learns a sparse network, indicating fewer parameters.
• Reduced model training time is observed due to the learned sparsity.

Conclusions:

• SPLR offers an effective approach for lifelong learning by disentangling shared and private representations.
• The method demonstrates significant improvements in parameter efficiency and training speed.
• SPLR provides a promising direction for developing more scalable and efficient lifelong learning systems.