发表机构
VARTA Microbattery GmbH（瓦尔塔微电池有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究持续学习中遗忘问题，提出将遗忘建模为任务间干扰，据此推导IGFA方法，该方法无重放、无Fisher，任务对齐时共享方向，冲突时保护方向，在不同任务情况表现良好，能实现无损保留或转移成本。
AI 中文摘要
持续学习通常依赖于诸如重放、弹性正则化或蒸馏等事后机制。本文认为遗忘应直接建模为任务间的干扰。在冻结特征机制下，学习新任务导致的遗忘正是旧任务上诱导的干扰能量。在深度网络中，通过路径平均曲率以最少额外前向传播可恢复相同量。当任务支持不相交时，遗忘可在结构上消除；当任务支持在冲突方向重叠时，非零失真下限不可避免。相同几何结构通过任务感知正交化最优合并模型。由此得出干扰门控功能分配（IGFA），一种无重放、无Fisher的方法，任务对齐时共享方向，冲突时保护方向。在各基准测试中，当任务在结构上可分离时，IGFA实现无损保留；当不可分离时，将不可避免的成本从不可逆遗忘转移到延迟但可恢复的可塑性上。在不同任务流上与最强的无重放结构基线匹配，在相似性使转移值得保留时优于无条件投影。
英文摘要
Continual learning commonly relies on post-hoc mechanisms such as replay, elastic regularization, or distillation. This work argues that forgetting should instead be modeled directly as interference between tasks. In the frozen-feature regime, forgetting from learning a new task is exactly the interference energy induced on the old task. In deep networks, the same quantity is recovered through path-averaged curvature with minimal additional forward passes. When task supports are disjoint, forgetting can be eliminated structurally and when task supports overlap in conflicting directions, a non-zero distortion floor is unavoidable. The same geometry optimally merges models through task-aware orthogonalization. From this analysis we derive Interference-Gated Functional Allocation (IGFA), a replay-free, Fisher-free method that shares directions when tasks align and protects them when they conflict. Across benchmarks, IGFA achieves lossless retention when tasks are structurally separable and moves unavoidable cost from irreversible forgetting into deferred but recoverable plasticity when they are not. It matches the strongest replay-free structural baselines on dissimilar-task streams and improves on unconditional projection when similarity makes transfer worth preserving.
Comments41 pages, 21 figures, 8 tables
