Data shift is a phenomenon present in many real-world applications, and while
there are multiple methods attempting to detect shifts, the task of localizing
and correcting the features originating such shifts has not been studied in
depth. Feature shifts can occur in many datasets, including in multi-sensor
data, where some sensors are malfunctioning, or in tabular and structured data,
including biomedical, financial, and survey data, where faulty standardization
and data processing pipelines can lead to erroneous features. In this work, we
explore using the principles of adversarial learning, where the information
from several discriminators trained to distinguish between two distributions is
used to both detect the corrupted features and fix them in order to remove the
distribution shift between datasets. We show that mainstream supervised
classifiers, such as random forest or gradient boosting trees, combined with
simple iterative heuristics, can localize and correct feature shifts,
outperforming current statistical and neural network-based techniques. The code
is available at this https URL

利用对抗性学习的原则，结合主流的监督分类器和简单迭代启发式方法，本研究探索了利用多个辨别器的信息来检测和修复数据集之间特征变化，以消除特征变化所带来的分布偏移，优于当前的统计和神经网络技术。

特征偏移检测与校正的对抗学习

Adversarial Learning for Feature Shift Detection and Correction

With the recent advancement of deep convolutional neural networks,
significant progress has been made in general face recognition. However, the
state-of-the-art general face recognition models do not generalize well to
occluded face images, which are exactly the common cases in real-world
scenarios. The potential reasons are the absences of large-scale occluded face
data for training and specific designs for tackling corrupted features brought
by occlusions. This paper presents a novel face recognition method that is
robust to occlusions based on a single end-to-end deep neural network. Our
approach, named FROM (Face Recognition with Occlusion Masks), learns to
discover the corrupted features from the deep convolutional neural networks,
and clean them by the dynamically learned masks. In addition, we construct
massive occluded face images to train FROM effectively and efficiently. FROM is
simple yet powerful compared to the existing methods that either rely on
external detectors to discover the occlusions or employ shallow models which
are less discriminative. Experimental results on the LFW, Megaface challenge 1,
RMF2, AR dataset and other simulated occluded/masked datasets confirm that FROM
dramatically improves the accuracy under occlusions, and generalizes well on
general face recognition. Code is available at
this https URL

采用端到端的神经网络方法，动态学习 mask，清除深度卷积网络中存在的遮挡干扰，并构造大量遮挡人脸图像，提高了人脸识别在遮挡情况下的准确性。