Title: LABEL CLEANING WITH A LIKELIHOOD RATIO TEST

Abstract: To collect large scale annotated data, it is inevitable to introduce label noise, i.e., incorrect class labels. A major challenge is to develop robust deep learning models that achieve high test performance despite training set label noise. We introduce a novel approach that directly cleans labels in order to train a high quality model. Our method leverages statistical principles to correct data labels and has a theoretical guarantee of the correctness. In particular, we use a likelihood ratio test (LRT) to flip the labels of training data. We prove that our LRT label correction algorithm is guaranteed to flip the label so it is consistent with the true Bayesian optimal classifier with high probability. We incorporate our label correction algorithm into the training of deep neural networks and train models that achieve superior testing performance on multiple public datasets.

1. INTRODUCTION:

Label noise is ubiquitous in real world data. It may be caused by unintentional mistakes of manual or automatic annotators (Yan et al., 2014; Veit et al., 2017) . It may also be introduced by malicious attackers (Steinhardt et al., 2017) . Noisy labels impair the performance of a model (Smyth et al., 1994; Brodley & Friedl, 1999) , especially a deep neural network, which tends to have strong memorization power (Frnay & Verleysen, 2014; Zhang et al., 2017) . Improving the robustness of a model to label noise is a crucial yet challenging task in many applications (Mnih & Hinton, 2012; Wu et al., 2018) . Existing methods mainly follow two directions, probabilistic reasoning and data selecting.

Probabilistic methods explicitly model a noise transition matrix, namely, the probability of one label being corrupted into another (Goldberger & Ben-Reuven, 2017; Patrini et al., 2017) . The transition matrix is often estimated from the data, and is used to re-calibrate the training loss or to correct the prediction. Explicit estimation of the transition matrix can be problematic due to the large variation of noise patterns, e.g., uniform noise, asymmetric noise, or mixtures. Furthermore, the transition matrix size is quadratic to the number of classes, making the estimation task prohibitive when the data has hundreds or even thousands of classes.

Data-selecting methods are agnostic of the underlying noise pattern. These methods gradually collect clean data whose labels are trustworthy (Malach & Shalev-Shwartz, 2017; Jiang et al., 2018; Han et al., 2018) . As more clean data are collected, the quality of the trained models improves. The major issue of these methods is the lack of a quantitative control of the quality of the collected clean data. Without a principled guideline, it is hard to find the correct data collection pace. An aggressive selection can unknowingly accumulate irreversible errors. On the other hand, an overly-conservative strategy can be very slow in training, or stops with insufficient clean data and mediocre models.

We propose a novel method with the benefit from both the probabilistic and the data-selecting approaches. Similar to data-selecting methods, our method continuously improves the purity of the data labels by correcting the noise-corrupted ones. Meanwhile, we improve the classifier using the updated labels. Our label correction algorithm is based on statistical principles and is theoretically guaranteed to deliver a high quality label set. Instead of explicitly estimating the transition matrix, the correction algorithm only depends on the prediction of the current model, denoted as f . Using an f -based likelihood ratio test, we determine whether the current label of each data should be corrected. Our main theorem proves that the label correction algorithm will clean a majority of noisy labels with high probability.

In practice, we incorporate the label correction algorithm into the training of deep neural networks. Our method iteratively updates the labels of the data while continuously training a deep neural network. To ensure the deep neural network does not overfit with noise labels that are yet to be corrected, we introduce a new retroactive loss term that regulates the model by enforcing its consistency with models in previous epochs. The rationale is that the model in an earlier training stage tends to fit the true signal rather than noise, although its overall performance is sub-optimal. Through experiments on various datasets with various noise patterns and levels, we show that our method produces robust neural network models with superior performance.

To the best of our knowledge, our method is the first to correct labels with theoretical guarantees. It is has advantages over both probabilistic methods and data-selecting methods. Compared with other data-selecting methods, it has a better quantitative control of the label quality and thus is less brittle when generalizing to different datasets and different noise patterns. Also note that we are not selecting clean data. Instead, we correct labels and always use the whole training set to train. This brings an additional advantage of fully leveraging the data. Compared with other probabilistic methods, our correction algorithm assumes a rather general family of underlying noise patterns and avoids an explicit estimation of the transition matrix.

1.1. RELATED WORK:

One representative strategy for handling label noise is to model and employ noise transition matrix to correct the loss. For example, Patrini et al. (2017) propose to correct the loss function with estimated noise pattern. The resulting loss is an unbiased estimator of the ground truth loss, and enables the trained model to achieve better performance. However, such an estimator relies on strong assumptions and could be inaccurate in certain scenarios. Reed et al. (2014) consider modeling the noise pattern with a hidden layer. The learning of this hidden layer is regularized with a feature reconstruction loss, yet without a guarantee that the true label distribution is learned. Another method mentioned in their work is to minimize the entropy of neural network output; however, this method tends to predict a single class. To address this weakness, Hendrycks et al. (2019) propose to utilize a small number of kosher data to pre-train a network and estimate the noise pattern. However, such clean data may not always be available in practice.

Alternatively, another direction proposes to design models that are intrinsically robust to noisy data. Crammer et al. (2009) introduce a regularized confidence weighting learning algorithm (AROW), where parameters of classifiers are assumed normally distributed and the mean and covariance of this distribution is updated during training. The idea here is to preserve the weight distribution as much as possible while requiring the model to maintain predictive ability. The follow-up work (Crammer & Lee 2010) proposes to improve this algorithm by herding the updating direction via specific velocity field (NHERD), achieving better performance. Both of these works impose parametric constraint on parameters, which could prevent classifiers from adapting to complex dataset. Arpit et al. (2017) show that deep neural networks tend to learn meaningful patterns before they over-fit to noisy ones. Based on this observation, they propose to add Gaussian or adversarial noise to input when training with noisy labels, and empirically show that such data perturbation is able to make the resulting model more robust. Other commonly adopted techniques, such as weight decay and dropout, are also shown to be effective in increasing the robustness of trained classifier (Arpit et al. 2017; Zhang et al. 2017) . However, the intrinsic reasons for this phenomenon still remains unclear and overfitting to noisy label is still inevitable. Apart from the above mentioned strategies, one recent work proposes to correct the corrupted labels during training. In particular, Tanaka et al. (2018) propose to jointly train the deep network and estimate the underlying true labels. While achieving improved performance, their method largely relies on the prior distribution and is difficult to deploy under cases where there is a large number of classes. In addition, there are a few other works attempting to select clean data while eliminating noisy ones during training (Malach & Shalev-Shwartz, 2017; Jiang et al., 2018; Han et al., 2018) . These methods demonstrate promising results, but lack quantitative control of the quality of the collected clean data.

Finally, beyond deep learning framework, there are several theoretic works that demonstrate the robustness of a variety of losses to label noise (Long & Servedio 2010; Natarajan et al. 2013; Ghosh et al. 2015; van Rooyen et al. 2015) . Following the work of (Wang & Chaudhuri 2018) , Gao et al. (2016) propose an algorithm that can converge to the Bayesian optimal classifier under different noisy settings. Moreover, they also provide in-depth discussion regarding the performance of k-nearest neighbor (KNN) classifiers. However, the problem with KNN is that it is computationally intensive and difficult to be incorporated into a learning context. Within the framework of deep learning, there are more efforts that need to be made to bridge theory and practice.

2. METHOD:

Our method has two synchronized modules, the training module and the label correction module.

The training module continues to learn a classifier based on the current labels. Meanwhile, the label correction module uses the prediction of the classifier to correct labels. We start with some preliminaries necessary for the exposition (Section 2.1). In Section 2.2, we explain our correction algorithm. It uses the prediction of the classifier (trained on noisy labels) for a likelihood ratio test. Based on the test result, it decides whether to correct the label of a data. Theoretically, we prove that under certain assumptions of the prediction, f , the algorithm will change the labels to the correct ones, i.e., the ones consistent with the Bayes optimal classifier (Theorem 1).

In Section 2.3, we present the overall training method. We incrementally train a deep neural network based on the corrected labels, y. Meanwhile, the network's prediction is used for label correction. To improve the quality of the prediction, we introduce a new loss term, called the retroactive loss. The goal is to regulate the model using models trained in earlier epoches, as they may be less overfitting with corrupted labels.

For the label correction module, we focus on a binary classifier. But the algorithm and the theoretical results can easily be generalized to the multiclass setting (Corollary 1).

2.1. PRELIMINARIES:

Let X be the feature space, Y = {0, 1} be the label space, and D be an unknown distribution on X × Y. The joint probability can be factored as D(x, y) = Pr(y|x)Pr(x). We denote by η(x) = Pr(y = 1|x) the true conditional probability. The Bayes risk of a classifier h : X → Y is R(D, h) = Pr (x,y)∼D (h(x) = y). A Bayes optimal classifier is the minimizer of the Bayes risk, i.e., h * = arg min h R(D, h). It can be calculated using the true conditional probability, η,

EQUATION

We assume the true conditional probability, η, satisfies the Tsybakov condition (also called the TNC condition) (Tsybakov et al., 2004) . This condition, also called margin assumption, stipulates that the uncertainty of η (region close to decision boundary at 1/2) and thus the Bayes optimal classifier is bounded. Definition 1 (Tsybakov Condition). There exist C > 0, λ > 0, and t 0 ∈ (0, 1 2 ], such that for all t ≤ t 0 ,

Pr η(x) - 1 2 < t ≤ Ct λ .

The noisy label setting. Instead of samples from D, we are given a sample set with noisy labels S = {(x, y)} where y is the possibly corrupted label based on the true label y. We assume a transition probability τ i→j = Pr( y = j|y = i), i.e., the chance a ground truth label y is flipped from class i to class j. For simplicity, we denote τ ij = τ i→j . The transition probabilities τ 01 and τ 10 are independent of the true joint distribution D and the feature x. We denote the conditional probability of the noisy labels as η(x) = Pr( y = 1|x). In short, we call η the noisy conditional probability. It is related linearly to the true conditional probability, η: 

EQUATION

2.2. THE LABEL CORRECTION ALGORITHM AND ITS THEORETICAL GUARANTEES:

Our label correction algorithm takes in a current neural network prediction f : X → [0, 1] (i.e., an estimation of η based on the noisy labels). For all training data and their current noisy label (x, y), the correction algorithm uses f to run a likelihood ratio test and to decide whether to flip the label according to the result. The goal of the likelihood test is to decide whether the null hypothesis, H 0 : y = y, is true. If yes, y is accepted as it is. Otherwise, we flip y, so that hopefully it becomes y. Formally, the likelihood ratio is defined as

EQUATION

We compare this likelihood ratio with a predetermined value ∆. If LR(f, x, y) ≤ ∆, we reject the null hypothesis and flip the label y new = 1 -y. Otherwise, the label remains unchanged, y new = y.

See Figure 2 for an illustration of the algorithm.

Note that the constant ∆ depends on the underlying noise pattern and f . Below we show that if we choose ∆ carefully, the label correction algorithm is guaranteed to make proper correction and clean most of the corrupted labels. However, in practice, ∆ is unknown and needs to be tuned.

Intuition. In the likelihood ratio (Eq. ( 3)), the numerator is the likelihood that the prediction f is consistent with the noisy label y. The denominator is the likelihood of the opposite case. When this ratio is smaller than 1, we know that the prediction of f is more likely to be inconsistent with y. But whether f agrees with y is not the hypothesis to test. To test the intended null hypothesis ( y = y), we need to check whether y is consistent with the true conditional distribution η, namely, the Bayes optimal classifier prediction h * (x). To this end, we assume f is a close enough approximate of η as it is trained on the noisy labels. This way, testing whether f agrees with y is close to testing whether η agrees with y, except that the threshold ∆ needs to be carefully chosen. Another issue we need to consider is that the ∆ is unknown. Our main theorem will bound the chance of failed correction by how close f approximates η and how close we can set ∆ to the perfect one.

2.2.1. FORMAL STATEMENT OF THE ALGORITHM AND THE THEOREM:

Assume the knowledge of some prediction function f (perhaps from a neural network). The label correction algorithm is given in Procedure 1. It checks the likelihood ratio with regard to a parameter ∆ = min( 1+τ01-τ10 1+τ10-τ01 , 1+τ10-τ01 1+τ01-τ10 ). If LR is no greater than ∆, we flip the label. Otherwise, y new is the same as y. Note that one only needs to know the difference τ 01 -τ 10 in order to know ∆. In practice, ∆ may be unknown and needs to be decided empirically.

Procedure 1 LRT-Correction Input: (x, y), f (x), ∆.

Output:

y new 1: if LR(f, x, y) ≤ ∆ then 2:

y new = 1 -y 3: else 4:

y new = y 5: end if Procedure 2 LRT-MultiCorrection Input: (x, y), f (x), ∆. Output: y new y * := arg max i [f (x)] i ; LR(f, x, y) := [f (x)] y [f (x)]y * ; 1: if LR(f, x, y) ≤ ∆ then 2:

y new = y * 3: else 4:

y new = y 5: end if

Our main theorem states that suppose the classifier prediction, f , is a close approximation of the noisy conditional probability, η. And suppose we can find a good enough ∆ that is close enough to the ideal ∆. Then there is a very good chance that our algorithm corrects most labels to the correct ones, i.e., the same as the Bayes optimal classifier prediction. Please note that here "proper correction" means that the new label, y new , is the same as the Bayes optimal classifier prediction, h * (x), instead of y. This is well justified as it means that the correction will give us a classifier as good as the Bayes optimal one. Theorem 1. Assume η satisfies the Tsybakov condition with constants C > 0 and λ > 0. Let h * denote the Bayes optimal classifier, and η the noisy conditional probability η(x) = (1 -τ 01τ 10 )η(x) + τ 01 . Let f be any classifier such that ||f -η|| ∞ ≤ for some > 0. Let ∆ = min( 1+τ01-τ10 1+τ10-τ01 , 1+τ10-τ01 1+τ01-τ10 ), and let ∆ > 0 be a constant such that ∆ ∈ [∆ -, ∆ + ]. If y new denotes the output of the LRT-Correction with y, x, f , and the given ∆ , then

Pr (x,y)∼D ( y new = h * ) ≤ C τ 10 -τ 01 2(1 -τ 10 -τ 01 ) + 2 λ .

If τ 01 = τ 10 , then Pr (x,y)∼D (

y new = h * ) = C (2 ) λ .

Intuition of the proof. We will prove two lemmas. The first lemma shows that when f is linear in η, i.e., f (x) = aη(x) + b, with known coefficients a, b, we can find a ∆ such that then the correction algorithm can be correct everywhere. The second lemma proves a more relaxed version. If f is exactly η and if we only know the difference between the transition probabilities, τ 01 -τ 10 , then we can bound the chance of mistakes of the correction algorithm. Finally, based on these two lemmas, careful case analysis, and the Tsybakov condition of the true conditional probability, η, we can prove the theorem. The complete proof can be found in Appendix A.

Multiclass setting. So far, all the description and theoretical results are based on a binary classification setting. However, the results can be generalized to a multiclass setting without any technical difficulties. Let y be the observed (possibly) corrupted label, f (x) be a classifier vector, where [f (x)] i (the ith coordinate of f (x)) specifies the confidence of assigning x to class i. The algorithm is presented in Procedure 2. Informally, we present the theoretical guarantee for multiclass LRT as Corollary 1. The proof is left for a journal version.

Corollary 1. Theorem 1 can be generalized to the multiclass setting, when using algorithm LRT-MultiCorrection.

2.3. TRAINING DEEP NEURAL NETWORKS WITH LRT-LABEL-CORRECTION:

Our training algorithm continuously trains a deep neural network while correcting the noisy labels. Procedure 3 is the pseudocode of the training method, called AdaCorr. It trains a neural network model iteratively. Each iteration includes both label correction and model training steps. In label correction step, the prediction of the current neural network, f , is used to run LRT test on all training data, and to correct their labels according to the test result. Since f is used to approximate the conditional probability η, we use the softmax layer output of the neural network as f . After the labels of all training data are updated, we use them to train the neural network incrementally. We continue this iterative procedure until the whole training converges. Train using L retro + L CE , with f and y 14: end for

We also have a burn-in stage in which we train the network using the original noisy labels for m epochs. During the burn-in stage, we use the original cross-entropy loss, L CE . Afterwards, we add an additional retroactive loss which will be explained below.

Training with retroactive loss. After the burn-in stage, we want to avoid overfitting of the neural network, so that its output better approximates η. To achieve this goal, we introduce a retroactive loss term L retro (f (x), y). The idea is to enforce the consistency between f and the prediction of the model at a previous epoch, f . It has been observed that a neural network at earlier training stage tends to learn the true pattern rather than to overfit the noise (Arpit et al., 2017) . Formally, the loss can be written as

Nc c=1 f c (x) log f c (x),

in which N c is the number of possible label classes. The training loss is the sum of the retroactive loss and the cross-entropy loss

L(f (x), y) = L retro (f (x), y) + L CE (f (x), y) = Nc c=1 f c (x) log f c (x) + Nc c=1 y c log f c (x).

In practice, we set f to be the prediction of the model at the m-th epoch. In other words, once the burn-in stage is finished, the training switches from L CE to L CE + L retro . And the model at the end of the burn-in stage is used for the retroactive loss. We also set the label correction to start slightly after the burn-in stage, say m + 10. The key hyperparameter is the starting epoch m. Another hyperparameter is ∆. We select both m and ∆ empirically. Ablation study in Section 4 shows that our method is robust to these hyperparameters.

3. EXPERIMENTS:

In this section we empirically evaluate our proposed method with several datasets, where noisy labels are injected according to specified noise transition matrices. Wu & Xiao 2015) . MNIST consists of 28 × 28 grayscale images with 10 categories. It contains 60,000 images, and we use 45,000 for training, 5,000 for validation and 10,000 for testing. CIFAR10 and CIFAR100 share the same 60,000 32×32×3 image data, with CIFAR10 having 10 categories while CIFAR100 having 100 categories. Similar to MNIST, we split 90% and 10% data from the official training set for the training and validation respectively, and use the official test set for testing. ModelNet40 contains 12,311 CAD models from 40 categories, where 8,859 are used for training, 984 for validation and the remaining 2,468 for testing. We follow the protocol of (Qi et al. (2017) ) to convert the CAD models into point clouds by uniformly sampling 1,024 points from the triangular mesh and normalizing them within a unit ball. In all experiments, we use early stopping on validation set to tune hyperparameters and to report the performance on test set.

Noise patterns. Following (Reed et al. 2014; Patrini et al. 2017) , we corrupt our labels artificially using a noise transition matrix T , where T ij = τ ij = Pr( y = j|y = i) is the probability that category i is flipped to category j. In our work we focus on two types of T : (1) uniform, where the true label i is flipped to other classes with equal probabilities, i.e., T ij = p/(N c -1) for i = j and T ii = 1 -p, where p is the noise level and N c is the class number; (2) pair flipping, where the

4. ABLATION STUDY AND DISCUSSION:

We conduct ablation study to see the significance of our contributions. We compare our method (LRT + L ce +L retro ) with two baselines: our method without the retroactive loss (LRT+L ce ) and using cross-entropy loss only without LRT-Correction (L ce Only). We report the test accuracy on CIFAR10 in Table 2 . We observe that adding LRT Label Correction to L ce alone helps improve the performance significantly. The numbers inside the parenthesis are the percentage of correct labels after the training and label correction. We observe that the LRT Correction corrected a large portion of noise labels. Also we observe adding the retroactive loss improves the method further in both test accuracy and label correction rates. We evaluate how different hyperparameters affect the performance of our method. We compare our method with different m, the length of the burn-in stage. We start introducing the retroactive loss after m epochs, and start label corrections after m + 10 epochs. The final testing accuracies are shown in Table 3 . We observe the performance of our method is rather robust w.r.t. different m's.

We choose m = 20 in this data set (CIFAR10) and similarly in other datasets. We also evaluate the performance on different ∆'s. ∆ is the unknown value for our likelihood ratio test. It controls how aggressive we would like to correct the labels. From Table 4 , we observe a bigger ∆ tends to give better results (as it is less aggressive in correcting labels). We observe 1/1.2 is the best one for CIFAR10. Similar values of optimal ∆ are found in other data sets. In general, we observe our hyperparameters are rather consistent across different datasets. This reveals a better generalization power of our method over other datasets and noise patterns. We believe this is due to the principled approach we take in label cleaning.

In Table 5 we show the performance of different methods trained on clean dataset. We observe that when without label noise, all the methods achieve very similar performance. However, note that on CIFAR10, CIFAR100 and ModelNet40, the performances of MentorNet and Forward are slightly inferior to the standard approach. The reason could be that MentorNet is designed specifically for handling noisy data, and would behave too conservatively under clean data setting; for Forward, although the data is clean, the estimated noise transition matrix is imperfect and still indicates a certain level of noise, leading to inferior performance. AdaCorr remains competitive in these experiments. 

5. CONCLUSION:

We propose a label correction algorithm to combat label noise. The correction algorithm performs a likelihood ratio test for each input label to decide whether to correct the label. Theoretically, we prove that our method corrects noisy labels with high probability. By combining with deep neural network training with a novel retroactive loss, our method can produce models robust to different noise patterns. Experiments on various datasets show that our method outperforms state-of-the-arts and is robust to hyperparameters.

:



Figures and Tables:

Figure 1: The overview of our method.

(a) Noisy labels and f . (b) Corrected labels and η. (c) LR for y = 1. (d) LR for y = 0.

Figure 2: An illustration of the label correction algorithm. ∆ is set to 1. (a): a corrupted sample and its corresponding classifier prediction f . (b): after correction, the labels are consistent with the true conditional probability, η. (c): likelihood ratio for y = 1. Data with x < 0 are corrected to η new = 0 as LR(x) are below ∆ = 1. (d): likelihood ratio for y = 0. Data with x > 0 are corrected to η new = 1 as LR(x) are below ∆ = 1.

AdaCorr Input: S = {x, y}, ∆, m, T 1: for epoch=1 to m do 2: Train neural network with L CE 3: end for 4: f = current model prediction 5: for epoch=m + 1 to T do 6: if epoch ≥ m + 10 then 7: f = current model prediction 8: for all (x, y) ∈ S do 9: y new = LRT-Correction(f ,(x, y),∆)

We use the following datasets: MNIST (LeCun & Cortes 2010), CIFAR10 (Krizhevsky et al. a), CIFAR100 (Krizhevsky et al. b) and ModelNet40 (Z.

± 0.1 99.5 ± 0.1 99.5 ± 0.1 99.5 ± 0.0 99.4 ± 0.2 99.5 ± 0.0 99.4 ± 0.3 CIFAR10 93.6 ± 0.3 93.5 ± 0.3 91.2 ± 0.4 93.7 ± 0.4 91.5 ± 0.4 93.6 ± 0.2 93.9 ± 0.2 CIFAR100 72.0 ± 0.4 72.0 ± 0.3 70.1 ± 0.3 72.0 ± 0.3 71.4 ± 0.3 72.2 ± 0.5 74.1 ± 0.0ModelNet40 87.6 ± 0.5 87.7 ± 0.5 60.3 ± 0.6 88.4 ± 0.6 85.0 ± 0.5 85.9 ± 0.4 88.4 ± 0.3In Figure3, we draw convergence curves on CIFAR10 with Uniform 0.4 noise level. On the left, we show the curves of our proposed AdaCorr method. The model continues to fit with the corrected labels (y new and the test accuracy on clean labels does not drop. This shows that the model and the label correction are improving in a harmonic fashion and do not collapse. On the right, we show the curves of the Standard model. Without label correction, the model overfits with the noisy label and loses on test data catastrophically.

Figure 3: Convergence curves for CIFAR10 with uniform 0.4 noisy level. Left: AdaCorr -training accuracy evaluated against the corrected label (y new ), testing accuracy against clean label. Right: Standard -training accuracy against noisy label ( y) and testing accuracy against clean label.

Effect of LRT Correction and L retro . The experiments are performed on CIFAR10 (accuracy in %). The number in parenthesis denotes the rate of correct labels after training.

Table content: <table><tr><td>Method</td><td>uniform 0.2</td><td>uniform 0.4</td><td>uniform 0.6</td><td>uniform 0.8</td><td>pair 0.4</td></tr><tr><td>L ce Only</td><td>87.5(80.0)</td><td>83.1(60.0)</td><td>76.4(40.0)</td><td>47.6(20.0)</td><td>84.5(60.0)</td></tr><tr><td>LRT+L ce</td><td>91.1(95.7)</td><td>87.9(91.2)</td><td>80.7(81.7)</td><td>47.3(47.1)</td><td>87.5(91.9)</td></tr><tr><td>LRT+L ce +L retro</td><td>91.0(95.7)</td><td>88.7(90.5)</td><td>81.2(82.5)</td><td>49.2(49.0)</td><td>89.2(91.3)</td></tr></table>

Effect of different m's. The experiments are performed on CIFAR10 (accuracy in %). The number in parenthesis denotes the rate of correct labels after training.

Table content: <table><tr><td>Noisy Type</td><td>Epoch 15</td><td>Epoch 20</td><td>Epoch 25</td><td>Epoch 30</td><td>Epoch 35</td><td>Epoch 40</td></tr><tr><td>uniform 0.4</td><td colspan="6">87.6(90.1) 88.7(90.5) 87.4(90.7) 86.7(90.6) 84.8(88.7) 84.1(87.2)</td></tr><tr><td>uniform 0.6</td><td colspan="6">79.4(81.0) 81.2(82.5) 80.9(81.9) 79.3(81.9) 79.1(81.8) 78.1(81.7)</td></tr><tr><td>pair 0.4</td><td colspan="6">75.8(80.0) 89.2(80.1) 90.8(87.0) 89.2(89.3) 88.2(90.1) 86.7(90.1)</td></tr></table>

Effect of ∆. The experiments are performed on CIFAR10, and the number in parenthesis denotes the rate of correct labels after flipping.

Table content: <table><tr><td>Noisy Type</td><td>1/1.0</td><td>1/1.2</td><td>1/1.5</td><td>1/2.0</td><td>1/2.5</td><td>1/3.0</td></tr><tr><td>uniform 0.4</td><td colspan="6">88.3(91.7) 88.7(90.5) 83.0(89.3) 77.1(86.7) 75.2(85.1) 75.5(84.0)</td></tr><tr><td>uniform 0.6</td><td colspan="6">79.9(81.0) 81.2(82.5) 80.9(81.9) 79.3(81.9) 79.1(81.8) 78.1(81.7)</td></tr><tr><td>pair 0.4</td><td colspan="6">88.2(92.2) 89.2(80.1) 84.4(89.8) 77.0(86.8) 76.4(85.1) 77.0(84.1)</td></tr></table>

The performance of different methods trained on clean dataset.

Table content: <table/>

Peer review (iclr2020):
This paper proposes a new method for correcting training label noise, using a likelihood ratio test on the predicted probability of the classifier trained on the noisy labels, and then proposes an algorithm for iteratively cleaning training labels and retraining a model. The paper also provides a theoretical guarantee for the probability of correctly re-labeling the training set, and provides empirical results showing that the proposed method significantly outperforms existing approaches for handling noisy training labels. Overall, I think the empirical results appear very strong, but think this paper is below the acceptance threshold due to three factors, in ranked order: - (1) The theoretical guarantee, which is positioned as a core contribution of the paper (and in fact claims it as *the first to correct labels with theoretical guarantees*, which is not true), is based on assumptions that seem overly strong; these are somewhat relaxed in a *Remark*, but this seems unproven and is a confusing presentation regardless. - (2) The theoretical bound itself is somewhat vacuous as it contains several constant factors that seem very material to the bound, but totally opaque to the reader. - (3) The experiments are very strong overall, which is a major plus for the paper; however, there are some questions about the hyperparameter tuning and some other points where more clarity could improve the strength of the empirical results Regarding (1): As a reader, my first natural reaction was to worry about circularity/degeneracy in the proposed method: basically, we are using the confidence (as a ratio of predicted cond. probabilities) of the model trained on the corrupted labels to correct those labels... if the labels are so corrupted that the model is also way off, then intuitively, this method should not work. I was wondering about how this situation would be bounded / handled. It turns out in Thm 1 that an incredibly strong assumption is made, namely that the model trained on the corrupted labels, f, is a linear function of the true model, with constants a, b known to some small degree of error epsilon (note that the theorem statement says that these constants are unknown- but it then assumes that Delta, which is set based on a and b, is known up to epsilon error). This seems like an incredibly strong assumption- and no context / motivation is given about why it should be taken as reasonable. Then, immediately after the Theorem, *Remark 2* states that this condition is not actually needed at all- but (a) then why not just strike it from Thm 1 statement, and (b) there does not seem to be any proof of this Remark in the appendix (where the proof of the main theorem itself is closer to a sketch than a standard proof...). Regarding (2): The bound produced in Thm 1 seems somewhat vacuous: letting 	au_{01} = 	au_{10}, then the probability of the label correction being erroneous is bounded by 8C(O(epsilon))^lambda. This quantity is presumably in (0,1/2], so it*s a small range to start... but it seems hard to get anything from this bound without some idea of what the constant factors (C, and those hidden in O(epsilon)) and lambda are. In particular, as presented, it seems implausible that epsilon- the error in specifying the Delta threshold- gets that small, in which case these constants become very important to know! Another way of phrasing this remark: many theoretical bounds have lots of unknown constant factors, but are ultimately just trying to expose some scaling with respect to one parameter, e.g. number of data points, and therefore the constants don*t need to be known that well for the statement to have some value. This doesn*t exactly seem to be the case here- therefore it seems hard to extract something from this statement (even ignoring the strong assumptions it is predicated on). Regarding (3): Overall, I think the empirical performance reported in Table 2, and overall thoroughness of the ablation in Section 4, are major strong points for the paper- the performance is very impressive! However I have a few questions, clarification of which would be very helpful in my mind: - (i) A major issue that seems to be raised in the earlier sections is that there is a hyperparameter Delta--the threshold for the likelihood ratio test--that everything depends on, and must be chosen empirically. Table 5 shows that the effect of choosing it is not crazy, but also clearly not insignificant. My question is: how is it chosen? On the validation dataset? And is this validation dataset also corrupted in the same way as the training dataset? If not, that seems like a major whole in the setup. - (ii) I also have a high level question for understanding: how is it possible for the various approaches to do so well with 0.6 and 0.8 noise level of uniform flipping? In the p=0.8 noise model, for example, the probability of a data point getting flipped to *any individual wrong label* is *greater than that of it being the correct label*. How is it possible to learn a model based on such a dataset? Was there some kind of pre-training? Was the validation set not corrupted? I don*t conceptually understand how the results shown are possible...? Overall, I think points in (1) and (3) could be helped with additional clarification and contextualization, and possibly (2) as well.