遗忘在盒子之外：从输入输出观察中清理可解释的深层信息网络
Forgetting Outside the Box: Scrubbing Deep Networks of Information Accessible from Input-Output Observations
摘要：
我们描述了一种用于从经过训练的深度网络中去除对一组训练数据的依赖性的过程，该过程改进了先前的方法并将其推广到不同的读出函数，并且可以扩展以确保在网络的激活中的遗忘。我们引入了一个新的约束，每个查询可以提取多少信息被遗忘的队列从一个黑盒网络，只有输入输出行为被观察。建议的遗忘过程中有一个确定性的部分来自微分方程的线性版本的模型，和一个随机的部分，确保信息破坏通过添加噪声量身定制的几何形状的损失景观。我们利用受神经切线核启发的DNN的激活和权重动态之间的联系来计算激活中的信息。
英文摘要：
We describe a procedure for removing dependency on a cohort of training data from a trained deep network that improves upon and generalizes previous methods to different readout functions and can be extended to ensure forgetting in the activations of the network. We introduce a new bound on how much information can be extracted per query about the forgotten cohort from a black-box network for which only the input-output behavior is observed. The proposed forgetting procedure has a deterministic part derived from the differential equations of a linearized version of the model, and a stochastic part that ensures information destruction by adding noise tailored to the geometry of the loss landscape. We exploit the connections between the activation and weight dynamics of a DNN inspired by Neural Tangent Kernels to compute the information in the activations.
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