Continual Learning Using Bayesian Neural Networks

Abstract

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. To address this issue, we propose a method, called Continual Bayesian Learning Networks (CBLN), which enables the networks to allocate additional resources to adapt to new tasks without forgetting the previously learned tasks. Using a Bayesian Neural Network, CBLN maintains a mixture of Gaussian posterior distributions that are associated with different tasks. The proposed method tries to optimise the number of resources that are needed to learn each task and avoids an exponential increase in the number of resources that are involved in learning multiple tasks. The proposed method does not need to access the past training data and can choose suitable weights to classify the data points during the test time automatically based on an uncertainty criterion. We have evaluated our method on the MNIST and UCR time-series datasets. The evaluation results show that our method can address the catastrophic forgetting problem at a promising rate compared to the state-of-the-art models.

arXiv comment10 pages

References in corpus (11)

• Overcoming catastrophic forgetting in neural networks
• What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
• A continual learning survey: Defying forgetting in classification tasks
• Weight Uncertainty in Neural Networks
• Continual Learning Through Synaptic Intelligence
• An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks
• Variational Continual Learning
• Overcoming Catastrophic Forgetting by Incremental Moment Matching
• Lifelong Learning with Dynamically Expandable Networks
• Deep Generative Dual Memory Network for Continual Learning
• Adversarial Phenomenon in the Eyes of Bayesian Deep Learning

Cited by in corpus (3)

• Continual Learning for Recurrent Neural Networks: an Empirical Evaluation
• Continual Learning Using Multi-view Task Conditional Neural Networks
• Sequoia: A Software Framework to Unify Continual Learning Research