提出新模型让边缘设备持续学习，减少标签需求且保持预测可靠性。
Active Continual Learning with Metaplastic Binary Bayesian Neural Networks
- 用受控先验松弛和不确定性自适应步长，防止模型遗忘与过饱和。
- 在1000任务的置换MNIST上持续学习，开放对象数据集节省32倍标签与更新。
- 适合资源受限的实时学习场景，尤其处理不平衡数据和压缩特征时。
始终在线的边缘系统需在计算资源受限下持续适应变化环境，并识别不可靠预测。贝叶斯二值神经网络在此场景具吸引力，但均值场伯努利后验在长时间非平稳流中易饱和，导致认知不确定性消失并冻结可塑性。本文提出BiMU，基于有界记忆变分目标，平衡稳定性、可塑性与遗忘。BiMU结合数据项与受控先验松弛，以及依赖不确定性的自适应步长，防止后验饱和并维持有效不确定性。该非退化后验支持完全在线、无需缓存的主动查询，通过蒙特卡洛分歧实现，显著降低标签请求与反向传播次数。在1000任务的置换MNIST上，模型持续学习并保持强分布外检测能力；在OpenLORIS-Object数据集上，于类别不平衡与特征压缩条件下，实现最高32倍的标签/更新节省，同时匹配准确率。
原文摘要 · Abstract (English)
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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