Regularization Shortcomings for Continual Learning

Abstract

In most machine learning algorithms, training data is assumed to be independent and identically distributed (iid). When it is not the case, the algorithm's performances are challenged, leading to the famous phenomenon of catastrophic forgetting. Algorithms dealing with it are gathered in the Continual Learning research field. In this paper, we study the regularization based approaches to continual learning and show that those approaches can not learn to discriminate classes from different tasks in an elemental continual benchmark: the class-incremental scenario. We make theoretical reasoning to prove this shortcoming and illustrate it with examples and experiments. Moreover, we show that it can have some important consequences on continual multi-tasks reinforcement learning or in pre-trained models used for continual learning. We believe that highlighting and understanding the shortcomings of regularization strategies will help us to use them more efficiently.

References in corpus (9)

• Scikit-learn: Machine Learning in Python
• Overcoming catastrophic forgetting in neural networks
• Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
• Continual Learning Through Synaptic Intelligence
• Progress & Compress: A scalable framework for continual learning
• Online Structured Laplace Approximations For Overcoming Catastrophic Forgetting
• Online Learned Continual Compression with Adaptive Quantization Modules
• Optimal Continual Learning has Perfect Memory and is NP-hard
• Continuum: Simple Management of Complex Continual Learning Scenarios

Cited by in corpus (9)

• Continual Learning for Recurrent Neural Networks: an Empirical Evaluation
• Online Class-Incremental Continual Learning with Adversarial Shapley Value
• Supervised Contrastive Replay: Revisiting the Nearest Class Mean Classifier in Online Class-Incremental Continual Learning
• Understanding Continual Learning Settings with Data Distribution Drift Analysis
• Continual Learning with Deep Artificial Neurons
• Continual Learning: Tackling Catastrophic Forgetting in Deep Neural Networks with Replay Processes
• Continual Learning in Deep Networks: an Analysis of the Last Layer
• Revisiting Catastrophic Forgetting in Class Incremental Learning
• Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph Classification