A Causal View on Robustness of Neural Networks

Abstract

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.

NeurIPS 2020

References in corpus (3)

• The Effectiveness of Data Augmentation in Image Classification using Deep Learning
• On Learning Invariant Representation for Domain Adaptation
• On Causal and Anticausal Learning

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• Embracing the Disharmony in Medical Imaging: A Simple and Effective Framework for Domain Adaptation
• Enhancing Model Robustness and Fairness with Causality: A Regularization Approach
• Causally Constrained Data Synthesis for Private Data Release
• Decoder-free Robustness Disentanglement without (Additional) Supervision