Matryoshka Continual Learning: Nested Representations for Continual Learning

Continual learners are prone to catastrophic forgetting, where learning new tasks overwrites the representations that earlier tasks depend on. To address this, we propose Matryoshka Continual Learning (MCL), which structures a single embedding as a sequence of nested sub-embeddings like Matryoshka dolls. The first few dimensions constitute a compact representation that encodes coarse features and is trained to support the classification task on its own. Each wider prefix contains the preceding one and extends it with progressively finer, constructive detail, yielding a coarse-to-fine hierarchy within a single vector. Embedding is learned through a teacher–student setup, where the teacher has access to privileged knowledge, forcing a student with the same architecture to follow its lead without access to that knowledge. Because MCL changes the representation rather than the learning rule, it plugs into existing CL methods. We evaluate MCL against 9 baselines on Seq-CIFAR-100, Seq-Tiny-ImageNet, and the Robotic Tactile Internet (RTI) datasets under Task-Incremental Learning (Task-IL) and Class-Incremental Learning (Class-IL) settings. In Task-IL, MCL attains 87.29% accuracy on Seq-CIFAR-100 and 77.59% on Seq-Tiny-ImageNet, 0.32 and 1.97 points above the second-best method, respectively. In Class-IL, MCL ranks the second-best method in accuracy on both benchmarks, while achieving the highest plasticity–stability score.