Deep learning models are nowadays broadly deployed to solve an incredibly large variety of tasks. Commonly, leveraging over the availability of "big data", deep neural networks are trained as black-boxes, minimizing an objective function at its output. This however does not allow control over the propagation of some specific features through the model, like gender or race, for solving some an uncorrelated task. This raises issues either in the privacy domain (considering the propagation of unwanted information) and of bias (considering that these features are potentially used to solve the given task). In this work we propose IRENE, a method to achieve information removal at the bottleneck of deep neural networks, which explicitly minimizes the estimated mutual information between the features to be kept ``private'' and the target. Experiments on a synthetic dataset and on CelebA validate the effectiveness of the proposed approach, and open the road towards the development of approaches guaranteeing information removal in deep neural networks.

通过在深度神经网络的瓶颈处显式地最小化要保密的特征与目标之间的估计互信息，本文提出了IRENE方法，以实现深度神经网络中信息的删除。实验验证了该方法的有效性，并为保证深度神经网络中信息删除的方法的开发铺平了道路。

深度神经网络瓶颈处的信息消除