Continual learning method reduces forgetting in streaming data
DLER: Dual-Logit Experience Replay for Online Continual Learning
Machine Learning
Summary
When computers learn from a stream of information that changes over time, they often forget what they learned before. The authors found that separating the model’s predictions into two parts helps: one part learns new things well but can get biased, while the other preserves previous knowledge. They created DLER, a way to train with both parts that also carefully selects which examples to remember. This method improves learning without forgetting, especially when memory is very limited.
What this means in practice
- •For machine learning engineers: Improve real-time AI models that learn from continuously changing data streams with limited memory.
- •For edge device developers: Develop adaptive AI on devices with limited storage by reducing forgetting of previous data during online training.
Authors
Huiwei Lin, Shanshan Feng, Kong Aik Lee
Abstract
Online continual learning aims to continuously train neural networks on a non-stationary data stream in a single pass. Rehearsal-based methods alleviate catastrophic forgetting by replaying stored samples, but the imbalance between current and previous data often leads to predictions biased toward current classes. Many approaches have been proposed to overcome the forgetting problem by correcting the bias; however, they still need to be improved in an online fashion. By decomposing the dot-product logits into an angle factor and a norm factor, our analysis reveals that the angle factor efficiently captures novel knowledge but is particularly susceptible to prediction bias, whereas the norm factor helps preserve historical knowledge. Based on these observations, we propose Dual-Logit Experience Replay (DLER), which exploits dual-logits for both model optimization and memory management. Specifically, DLER learns current samples using cosine logits and incorporates additional dot-product logits when replaying previous samples to mitigate prediction bias. To strengthen dual-logit learning under limited replay memory, an auxiliary supervised contrastive objective promotes angular compactness within each class, extending corrections learned from limited replay samples to other samples of the same class. Meanwhile, we introduce a memory update strategy that preferentially retains samples for which dot-product prediction assigns a higher probability to the correct class than cosine prediction, aligning sample selection with dual-logit training. Extensive experiments on three datasets demonstrate the effectiveness of DLER, particularly under limited memory budgets.