Exploiting Task Relationships for Continual Learning Using Transferability-Aware Task Embeddings

Continual learning (CL) has been an essential topic in the contemporary application of deep neural networks, where catastrophic forgetting (CF) can impede a model's ability to acquire knowledge progressively. Existing CL strategies primarily address CF by regularizing model updates or separating task-specific and shared components. However, these methods focus on task model elements while overlooking the potential of leveraging inter-task relationships for learning enhancement. To address this, we propose a transferability-aware task embedding named H-embedding and train a hypernet under its guidance to learn task-conditioned model weights for CL tasks. Particularly, H-embedding is introduced based on an information theoretical transferability measure and is designed to be online and easy to compute. The framework is also characterized by notable practicality, which only requires storing a low-dimensional task embedding for each task, and can be efficiently trained in an end-to-end way. Extensive evaluations and experimental analyses on datasets including Permuted MNIST, Cifar10/100, and ImageNet-R demonstrate that our framework performs prominently compared to various baseline methods, displaying great potential in exploiting intrinsic task relationships.