NORACL: Neurogenesis for Oracle-free Resource-Adaptive Continual Learning
In a continual learning setting, we require a model to be plastic enough to learn a new task and stable enough to not disturb previously learned capabilities. We argue that this dilemma has an architectural root. A finite network has limited representational and plastic resources, yet the required capacity depends on properties of the future task stream that are unknown: how many tasks will be encountered, and how much they overlap in feature space. Regularization-based methods preserve past knowledge within fixed-capacity architectures and therefore implicitly rely on an oracle architecture sized for this unknown future. When tasks are only weakly related, fixed architectures progressive...
| Authors | Karthik Charan Raghunathan, Christian Metzner, Laura Kriener et al. |
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| Primary category | cs.LG |
| Published | 29 April 2026 |
| Last updated at source | 16 July 2026 |
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