Subspace Distillation for Continual Learning
This addresses the problem of preserving prior knowledge while learning new tasks for AI systems, representing an incremental improvement in continual learning methods.
The paper tackles catastrophic forgetting in continual learning by proposing a novel knowledge distillation technique that models the manifold structure of neural network latent/output spaces using linear subspaces, achieving improved performance over existing methods on datasets like Pascal VOC and Tiny-Imagenet.
An ultimate objective in continual learning is to preserve knowledge learned in preceding tasks while learning new tasks. To mitigate forgetting prior knowledge, we propose a novel knowledge distillation technique that takes into the account the manifold structure of the latent/output space of a neural network in learning novel tasks. To achieve this, we propose to approximate the data manifold up-to its first order, hence benefiting from linear subspaces to model the structure and maintain the knowledge of a neural network while learning novel concepts. We demonstrate that the modeling with subspaces provides several intriguing properties, including robustness to noise and therefore effective for mitigating Catastrophic Forgetting in continual learning. We also discuss and show how our proposed method can be adopted to address both classification and segmentation problems. Empirically, we observe that our proposed method outperforms various continual learning methods on several challenging datasets including Pascal VOC, and Tiny-Imagenet. Furthermore, we show how the proposed method can be seamlessly combined with existing learning approaches to improve their performances. The codes of this article will be available at https://github.com/csiro-robotics/SDCL.