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arXiv · 2605.30198

Active Continual Learning with Metaplastic Binary Bayesian Neural Networks

Kellian Cottart·Théo Ballet·Djohan Bonnet·Damien Querlioz
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Abstract

Always-on edge systems must keep learning as conditions change under tight compute budgets and must detect unreliable predictions. Bayesian binary neural networks are attractive in this setting, but mean-field Bernoulli posteriors can saturate on long non-stationary streams, wiping out epistemic uncertainty and freezing plasticity. We propose BiMU, derived from a bounded-memory variational objective that balances stability, plasticity, and forgetting. BiMU combines a data term with controlled relaxation toward the prior and an uncertainty-dependent step size that prevents saturation and sustains informative uncertainty. This non-degenerate posterior enables fully online, buffer-free active querying via Monte Carlo disagreement, reducing label queries and backpropagation updates under imbalance. BiMU sustains learning and strong OOD detection on 1000-tasks Permuted-MNIST, and on OpenLORIS-Object achieves up to 32$\times$ label/update savings at matched accuracy under class imbalance and feature compression.

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Kellian Cottart, Théo Ballet, Djohan Bonnet, Damien Querlioz. 2026-05-28. Active Continual Learning with Metaplastic Binary Bayesian Neural Networks. https://arxiv.org/abs/2605.30198

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