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concepts/smg-sequential-adaptation-flow.md
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title: "SMG Sequential Adaptation Flow (SMG 序列适应流)"
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created: 2026-07-10
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updated: 2026-07-10
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type: concept
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tags: ["catastrophic-forgetting", "continual-learning", "smg", "orthogonal-projection"]
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sources: ["[[smg-framework-2026|SMG (Cheng et al., 2026)]]"]
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---
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# SMG Sequential Adaptation Flow (SMG 序列适应流)
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SMG 框架对**灾难性遗忘**的非渐近完全消除方案——通过几何投影保证下游更新不影响历史学习。
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## 核心机制
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当模型需要学习新任务时,SMG 不依赖经验微调,而是:
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1. **记录历史水平支架(Horizontal Carriage)**:保存先前任务在 SVDχ 空间中学习的水平方向基
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2. **正交投影**:将新任务的下游更新投影到历史水平支架的**正交补**上
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3. **仅在未占用方向学习**:新知识被编码在严格正交于旧知识的方向上
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```
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Δ_new = P_{orth(H_old)} (∇_loss)
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```
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## 定理:遗忘消除
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**Theorem (Non-Asymptotic Forgetting Elimination)**:在 SMG Sequential Adaptation Flow 下,对任意任务序列,之前学到的 SVDχ 分量的 Fisher 信息在后续更新中**严格保持不变**——灾难性遗忘在非渐近意义下完全消除。
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## 与经验方法的对比
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| 方法 | 原理 | 保证 |
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|------|------|------|
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| EWC | Fisher 加权正则化 | 渐近近似 |
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| PackNet | 硬参数隔离 | 需要预分配 |
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| Replay | 数据回放 | 存储昂贵 |
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| **SMG Sequential Flow** | **几何正交投影** | **非渐近完全** |
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## 参考
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- [[statistically-meaningful-geometry|SMG]]
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- [[quarantining-theorem|Quarantining Theorem]]
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- [[statistically-verifiable-directions|SVDχ]]
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