CL3: Generalization of Contrastive Loss for Lifelong Learning.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Deep Learning

Background:

• Lifelong learning enables gradual learning in non-stationary environments, mimicking human learning efficiency and robustness.
• Neural networks face catastrophic forgetting, losing past knowledge when acquiring new concepts from sequential data.

Purpose of the Study:

• To propose a novel knowledge distillation algorithm to mitigate catastrophic forgetting in neural networks.
• To enable neural networks to preserve past knowledge while learning new concepts incrementally.

Main Methods:

• Developed a generalized contrastive distillation strategy.
• Utilized contrastive learning to help neural networks retain past knowledge.
• Minimized semantic drift by maintaining a consistent embedding space.

Main Results:

• The proposed method effectively tackles catastrophic forgetting.
• Semantic drift was minimized, preserving the integrity of learned knowledge.
• Feature distribution compactness was ensured for accommodating new tasks.
• Achieved improved performance in class-incremental, task-incremental, and domain-incremental learning scenarios.

Conclusions:

• The novel contrastive distillation algorithm successfully addresses catastrophic forgetting in lifelong learning.
• The method enhances neural network adaptability and knowledge retention in incremental learning settings.
• This approach offers a robust solution for supervised incremental learning challenges.