Mixture-of-Variational-Experts for Continual Learning

25 Oct 2021  ·  Heinke Hihn, Daniel A. Braun ·

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.

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Domain-IL Continual Learning Cifar100 (10 tasks) HVCL Average Accuracy 37.20 # 1
Domain-IL Continual Learning Cifar10 (5 tasks) HVCL Average Accuracy 81.00 # 1
Domain-IL Continual Learning MNIST (5 Tasks) HVCL Average Accuracy 98.60 # 1
Domain-IL Continual Learning Permuted MNIST HVCL Average Accuracy 97.47 # 1