Mixture-of-Variational-Experts for Continual Learning

TL;DR

This paper introduces a hierarchical information-theoretic optimality principle for continual learning, leading to a Mixture-of-Variational-Experts layer that mitigates forgetting across various learning paradigms.

Contribution

It proposes a novel optimality principle and a neural network layer that reduces forgetting in continual learning across multiple problem types.

Findings

Competitive performance in continual supervised learning

Effective in continual reinforcement learning

General formulation applicable to diverse learning tasks

Abstract

One weakness of machine learning algorithms is the poor ability of models to solve new problems without forgetting previously acquired knowledge. The Continual Learning (CL) paradigm has emerged as a protocol to systematically investigate settings where the model sequentially observes samples generated by a series of tasks. In this work, we take a task-agnostic view of continual learning and develop a hierarchical information-theoretic optimality principle that facilitates a trade-off between learning and forgetting. We discuss this principle from a Bayesian perspective and show its connections to previous approaches to CL. Based on this principle, we propose a neural network layer, called the Mixture-of-Variational-Experts layer, that alleviates forgetting by creating a set of information processing paths through the network which is governed by a gating policy. Due to the general formulation based on generic utility functions, we can apply this optimality principle to a large variety of learning problems, including supervised learning, reinforcement learning, and generative modeling. We demonstrate the competitive performance of our method in continual supervised learning and in continual reinforcement learning.