51 lines
1.8 KiB
Markdown
51 lines
1.8 KiB
Markdown
---
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title: "MC Dropout (Monte Carlo Dropout)"
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created: 2026-06-10
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updated: 2026-06-10
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type: concept
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tags: ["bayesian-deep-learning", "uncertainty-quantification", "dropout"]
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sources: ["[[principled-uncertainty-clinical-ai]]"]
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---
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# MC Dropout (Monte Carlo Dropout)
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**MC Dropout**（Gal & Ghahramani, 2016）是实践中最简便的贝叶斯不确定性估计方法：训练时使用 Dropout 正则化，推理时**保持 Dropout 激活**并执行 T 次随机前向传播。
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## 理论基础
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训练时使用 Dropout 的神经网络近似于深度高斯过程中的变分贝叶斯推断。推理时保持 Dropout 等价于从近似后验中采样。
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## 算法
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```
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for t = 1 to T:
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z_t = f_theta(x) with dropout active
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y_hat = (1/T) * sum z_t # 预测均值
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sigma^2_epistemic = (1/T) * sum (z_t - y_hat)^2 # 认知不确定性
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```
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## 参数选择
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- **Dropout 概率 p**：通常 0.3-0.5。在 [[principled-uncertainty-clinical-ai|Principled Uncertainty in Clinical AI]] 中 p = 0.3
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- **采样次数 T**：T 越大估计越稳定，T 越小推理越快，通常 T = 10-50
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- **Dropout 位置**：通常在每一层后应用
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## 优势与局限
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| 优点 | 局限 |
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|------|------|
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| 实现极简（无需修改训练代码） | 近似质量受 Dropout 概率影响 |
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| 与现有架构兼容 | 推理时计算量为 T 倍 |
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| 提供认知不确定性估计 | 不能直接估计随机不确定性 |
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## 与其他方法的比较
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- **Deep Ensembles**：更准确但计算成本更高（M 个独立网络）
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- **[[bayesian-deep-learning|Bayes by Backprop]]**：更严格但训练不稳定
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- **[[variational-autoencoder|VAE]]**：学习潜分布，适合结构化潜空间
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## 参考
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- [[principled-uncertainty-clinical-ai|Principled Uncertainty in Clinical AI]]
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- [[epistemic-uncertainty|认知不确定性]]
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- [[bayesian-deep-learning|贝叶斯深度学习]]
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