A noisy training set usually leads to the degradation of the generalization
and robustness of neural networks. In this paper, we propose a novel
theoretically guaranteed clean sample selection framework for learning with
noisy labels. Specifically, we first present a Scalable Penalized Regression
(SPR) method, to model the linear relation between network features and one-hot
labels. In SPR, the clean data are identified by the zero mean-shift parameters
solved in the regression model. We theoretically show that SPR can recover
clean data under some conditions. Under general scenarios, the conditions may
be no longer satisfied; and some noisy data are falsely selected as clean data.
To solve this problem, we propose a data-adaptive method for Scalable Penalized
Regression with Knockoff filters (Knockoffs-SPR), which is provable to control
the False-Selection-Rate (FSR) in the selected clean data. To improve the
efficiency, we further present a split algorithm that divides the whole
training set into small pieces that can be solved in parallel to make the
framework scalable to large datasets. While Knockoffs-SPR can be regarded as a
sample selection module for a standard supervised training pipeline, we further
combine it with a semi-supervised algorithm to exploit the support of noisy
data as unlabeled data. Experimental results on several benchmark datasets and
real-world noisy datasets show the effectiveness of our framework and validate
the theoretical results of Knockoffs-SPR. Our code and pre-trained models will
be released.

本文提出了一种用于学习带噪标签的理论上保证的干净样本选择框架，通过 Scalable Penalized Regression（SPR）方法建模网络特征与 one-hot 标签之间的线性关系来识别干净数据，并通过数据自适应方法的 Knockoffs-SPR 控制假阳率，结果表明该方法有效。

Knockoffs-SPR：带噪声标签学习中的干净样本选择

Knockoffs-SPR: Clean Sample Selection in Learning with Noisy Labels

With the rapid growth of crowdsourcing platforms it has become easy and
relatively inexpensive to collect a dataset labeled by multiple annotators in a
short time. However due to the lack of control over the quality of the
annotators, some abnormal annotators may be affected by position bias which can
potentially degrade the quality of the final consensus labels. In this paper we
introduce a statistical framework to model and detect annotator's position bias
in order to control the false discovery rate (FDR) without a prior knowledge on
the amount of biased annotators - the expected fraction of false discoveries
among all discoveries being not too high, in order to assure that most of the
discoveries are indeed true and replicable. The key technical development
relies on some new knockoff filters adapted to our problem and new algorithms
based on the Inverse Scale Space dynamics whose discretization is potentially
suitable for large scale crowdsourcing data analysis. Our studies are supported
by experiments with both simulated examples and real-world data. The proposed
framework provides us a useful tool for quantitatively studying annotator's
abnormal behavior in crowdsourcing data arising from machine learning,
sociology, computer vision, multimedia, etc.

本文中，我们开发了一个统计框架来建模和检测标注者的位置偏差，并采用新型 knockoff 过滤器和基于反比例尺度动力学的算法来控制虚警率，为定量研究众包数据中的标注者异常行为提供了一种有用的工具。