Generative Replay for Equilibrium Propagation: Toward Edge-Friendly Continual Learning in Hopfield Networks

The paper introduces Energy-Based Generative Replay (EB-Gen) for Equilibrium Propagation (EqProp) trained Hopfield Networks, enabling continual learning without external data buffers or architectural overhead. EB-Gen leverages the network’s intrinsic generative dynamics to produce pseudo‑samples from a frozen copy of the model, allowing internal rehearsal. Experiments on split MNIST and split CIFAR‑10 show that EB-Gen improves stability and plasticity, achieving 75 % and 64 % average accuracy respectively, outperforming several baseline methods.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

• benchmarks