论文标题
关于神经网络鲁棒性的因果观点
A Causal View on Robustness of Neural Networks
论文作者
论文摘要
我们提出了关于神经网络对输入操作的鲁棒性的因果观点，这不仅适用于传统的分类任务，还适用于一般测量数据。基于这种观点，我们设计了一个深层的因果操纵增强模型（Deep Cama），该模型明确地模拟了某些原因可能导致观察到效应变化的某些原因的操作。我们进一步开发数据增强和测试时间微调方法，以改善CAMA的鲁棒性。与歧视性深神经网络相比，我们提出的模型表现出对看不见的操纵的较高鲁棒性。作为副产品，我们的模型实现了分离的表示，将操纵的表示与其他潜在原因的表示。
We present a causal view on the robustness of neural networks against input manipulations, which applies not only to traditional classification tasks but also to general measurement data. Based on this view, we design a deep causal manipulation augmented model (deep CAMA) which explicitly models possible manipulations on certain causes leading to changes in the observed effect. We further develop data augmentation and test-time fine-tuning methods to improve deep CAMA's robustness. When compared with discriminative deep neural networks, our proposed model shows superior robustness against unseen manipulations. As a by-product, our model achieves disentangled representation which separates the representation of manipulations from those of other latent causes.
