Overcoming Catastrophic Forgetting via Direction-Constrained Optimization

TL;DR

This paper introduces Direction-Constrained Optimization (DCO), a novel method for continual learning that constrains model parameters within task-specific cones to prevent catastrophic forgetting, showing improved performance over existing methods.

Contribution

The paper proposes DCO, a new regularization-based continual learning algorithm using autoencoders to approximate and constrain top principal directions, reducing forgetting.

Findings

DCO outperforms state-of-the-art regularization methods.

Autoencoders effectively identify task-specific principal directions.

Memory-efficient DCO-COMP maintains performance with fixed memory size.

Abstract

This paper studies a new design of the optimization algorithm for training deep learning models with a fixed architecture of the classification network in a continual learning framework. The training data is non-stationary and the non-stationarity is imposed by a sequence of distinct tasks. We first analyze a deep model trained on only one learning task in isolation and identify a region in network parameter space, where the model performance is close to the recovered optimum. We provide empirical evidence that this region resembles a cone that expands along the convergence direction. We study the principal directions of the trajectory of the optimizer after convergence and show that traveling along a few top principal directions can quickly bring the parameters outside the cone but this is not the case for the remaining directions. We argue that catastrophic forgetting in a continual learning setting can be alleviated when the parameters are constrained to stay within the intersection of the plausible cones of individual tasks that were so far encountered during training. Based on this observation we present our direction-constrained optimization (DCO) method, where for each task we introduce a linear autoencoder to approximate its corresponding top forbidden principal directions. They are then incorporated into the loss function in the form of a regularization term for the purpose of learning the coming tasks without forgetting. Furthermore, in order to control the memory growth as the number of tasks increases, we propose a memory-efficient version of our algorithm called compressed DCO (DCO-COMP) that allocates a memory of fixed size for storing all autoencoders. We empirically demonstrate that our algorithm performs favorably compared to other state-of-art regularization-based continual learning methods.