Online Continual Learning via the Knowledge Invariant and Spread-out Properties

Abstract

The goal of continual learning is to provide intelligent agents that are capable of learning continually a sequence of tasks using the knowledge obtained from previous tasks while performing well on prior tasks. However, a key challenge in this continual learning paradigm is catastrophic forgetting, namely adapting a model to new tasks often leads to severe performance degradation on prior tasks. Current memory-based approaches show their success in alleviating the catastrophic forgetting problem by replaying examples from past tasks when new tasks are learned. However, these methods are infeasible to transfer the structural knowledge from previous tasks i.e., similarities or dissimilarities between different instances. Furthermore, the learning bias between the current and prior tasks is also an urgent problem that should be solved. In this work, we propose a new method, named Online Continual Learning via the Knowledge Invariant and Spread-out Properties (OCLKISP), in which we constrain the evolution of the embedding features via Knowledge Invariant and Spread-out Properties (KISP). Thus, we can further transfer the inter-instance structural knowledge of previous tasks while alleviating the forgetting due to the learning bias. We empirically evaluate our proposed method on four popular benchmarks for continual learning: Split CIFAR 100, Split SVHN, Split CUB200 and Split Tiny-Image-Net. The experimental results show the efficacy of our proposed method compared to the state-of-the-art continual learning algorithms.

arXiv comment30 pages

References in corpus (15)

• Auto-Encoding Variational Bayes
• Distilling the Knowledge in a Neural Network
• Overcoming catastrophic forgetting in neural networks
• A Simple Framework for Contrastive Learning of Visual Representations
• Fully Convolutional Networks for Semantic Segmentation
• A continual learning survey: Defying forgetting in classification tasks
• Progressive Neural Networks
• Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence
• Continual Learning Through Synaptic Intelligence
• Lifelong Learning with Dynamically Expandable Networks
• Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference
• Don't forget, there is more than forgetting: new metrics for Continual Learning
• Adaptive feature recombination and recalibration for semantic segmentation with Fully Convolutional Networks
• M2KD: Multi-model and Multi-level Knowledge Distillation for Incremental Learning
• La-MAML: Look-ahead Meta Learning for Continual Learning