Continual learning (CL), the capability of systems to adapt over time without losing prior knowledge, presents a significant challenge. Neural networks, while adept at processing large amounts of data, can often suffer from catastrophic forgetting, when learning new information may erase what was previously learned. This becomes extremely problematic in scenarios with limited data retention capabilities or extensive task sequences.
Traditional strategies to combat catastrophic forgetting have centred around rehearsal and multitask learning, utilizing bounded memory buffers to store and replay past examples, or sharing representations across tasks. While these methods are helpful, they can often lead to overfitting and lack effective generalization across varied tasks. This struggle intensifies in low-buffer scenarios, where insufficient data fails to represent all past learnings.
To address this issue, researchers from the Eindhoven University of Technology and Wayve have put forth a new framework known as IMEX-Reg (Implicit-Explicit Regularization). This approach blends contrastive representation learning (CRL) with consistency regularization to enable more robust generalization. Specifically, it emphasizes preserving past data and ensures that the learning process inherently discourages forgetting by enhancing the model’s ability to generalize across tasks and conditions.
IMEX-Reg functions on two levels: it employs CRL to motivate the model to recognise and emphasise beneficial features across various data representations and uses positive and negative pairings to improve its predictions. Consistent regularization helps align the model’s outputs more closely with real-world data distributions, thereby preserving accuracy even when data is limited.
The empirical results highlight the effectiveness of IMEX-Reg, showing that it outperforms existing approaches on numerous benchmarks. Specifically, in low-buffer regimes, IMEX-Reg curbs the extent of forgetting and improves task accuracy in comparison to traditional rehearsal-based techniques. Even in situations with only 200 memory slots, IMEX-Reg achieves top-1 accuracy improvements of 9.6% and 37.22% on challenging datasets like Seq-CIFAR100 and Seq-TinyImageNet, respectively.
IMEX-Reg is also demonstrated to be resilient against both natural and adversarial disruptions, crucial for dynamic real-world applications where data corruption or malicious attacks might happen. This robustness, combined with less task-recency bias, positions IMEX-Reg as a progressive solution that retains past knowledge and ensures equal learning across all tasks.
In conclusion, IMEX-Reg provides a significant improvement to continual learning by integrating strong inductive biases with groundbreaking regularization techniques. Its performance on various metrics and conditions testifies to its capacity to foster more adaptable, stable, and robust learning systems, creating a new gold standard for prospective advancements in this field. Accordingly, IMEX-Reg shows potential in improving performance in continual learning applications and paves the way for more intelligent, resilient neural networks.
