Memory Efficient Continual Learning with Transformers
This addresses the problem of memory-efficient continual learning for practitioners using large pre-trained models, though it appears incremental as it builds on existing Adapter techniques.
The paper tackles catastrophic forgetting in continual learning by proposing a method using pre-trained Transformers with Adapters, achieving good predictive performance without retraining or increasing parameters, and demonstrating faster inference compared to state-of-the-art Adapter-based methods.
In many real-world scenarios, data to train machine learning models becomes available over time. Unfortunately, these models struggle to continually learn new concepts without forgetting what has been learnt in the past. This phenomenon is known as catastrophic forgetting and it is difficult to prevent due to practical constraints. For instance, the amount of data that can be stored or the computational resources that can be used might be limited. Moreover, applications increasingly rely on large pre-trained neural networks, such as pre-trained Transformers, since the resources or data might not be available in sufficiently large quantities to practitioners to train the model from scratch. In this paper, we devise a method to incrementally train a model on a sequence of tasks using pre-trained Transformers and extending them with Adapters. Different than the existing approaches, our method is able to scale to a large number of tasks without significant overhead and allows sharing information across tasks. On both image and text classification tasks, we empirically demonstrate that our method maintains a good predictive performance without retraining the model or increasing the number of model parameters over time. The resulting model is also significantly faster at inference time compared to Adapter-based state-of-the-art methods.