Continual Learning Using Bayesian Neural Networks

Understand

Continual learning models allow to learn and adapt to new changes and tasks over time.

• However, in continual and sequential learning scenarios in which the models are trained using different data with various distributions, neural networks tend to forget the previously learned knowledge.
• This phenomenon is often referred to as catastrophic forgetting.
• The catastrophic forgetting is an inevitable problem in continual learning models for dynamic environments.