LEVERAGING UNLABELED DATA TO TRACK MEMORIZATION

Abstract

Deep neural networks may easily memorize noisy labels present in real-world data, which degrades their ability to generalize. It is therefore important to track and evaluate the robustness of models against noisy label memorization. We propose a metric, called susceptibility, to gauge such memorization for neural networks. Susceptibility is simple and easy to compute during training. Moreover, it does not require access to ground-truth labels and it only uses unlabeled data. We empirically show the effectiveness of our metric in tracking memorization on various architectures and datasets and provide theoretical insights into the design of the susceptibility metric. Finally, we show through extensive experiments on datasets with synthetic and real-world label noise that one can utilize susceptibility and the overall training accuracy to distinguish models that maintain a low memorization on the training set and generalize well to unseen clean data.

1. INTRODUCTION

Deep neural networks are prone to memorizing noisy labels in the training set, which are inevitable in many real world applications (Frénay & Verleysen, 2013; Zhang et al., 2016; Arpit et al., 2017; Song et al., 2020a; Nigam et al., 2020; Han et al., 2020; Zhang et al., 2021a; Wei et al., 2021) . Given a new dataset that contains clean and noisy labels, one refers to the subset of the dataset with correct labels (respectively, with incorrect labels due to noise), as the clean (respectively, noisy) subset. When neural networks are trained on such a dataset, it is important to find the sweet spot from no fitting at all to fitting every sample. Indeed, fitting the clean subset improves the generalization performance of the model (measured by the classification accuracy on unseen clean data), but fitting the noisy subset, referred to as "memorization"foot_0 , degrades its generalization performance. New methods have been introduced to address this issue (for example, robust architectures (Xiao et al., 2015; Li et al., 2020) , robust objective functions (Li et al., 2019; Ziyin et al., 2020) , regularization techniques (Zhang et al., 2017; Pereyra et al., 2017; Chen et al., 2019; Harutyunyan et al., 2020) , and sample selection methods (Nguyen et al., 2019) ), but their effectiveness cannot be assessed without oracle access to the ground-truth labels to distinguish the clean and the noisy subsets, or without a clean test set. Our goal in this paper is to track memorization during training without any access to ground-truth labels. To do so, we sample a subset of the input data and label it uniformly at random from the set of all possible labels. The samples can be taken from unlabeled data, which is often easily accessible, or from the available training set with labels removed. This new held-out randomly-labeled set is created for evaluation purposes only, and does not affect the original training process. First, we compare how different models fit the held-out randomly-labeled set after multiple steps of training on it. We observe empirically that models that have better accuracy on unseen clean test data show more resistance towards memorizing the randomly-labeled set. This resistance is captured by the number of steps required to fit the held-out randomly-labeled set. In addition, through our theoretical convergence analysis on this set, we show that models with high/low test accuracy are resistant/susceptible to memorization, respectively.



Fitting samples that have incorrect random labels is done by memorizing the assigned label for each particular sample. Hence, we refer to it as memorization, in a similar spirit as Feldman & Zhang (2020).1

