Open-set Label Noise Can Improve Robustness Against Inherent Label Noise
Authors: Hongxin Wei
Lue Tao
RENCHUNZI XIE
Bo An
Venue: NeurIPS 2021 Poster (2021)
Keywords: noisy labels
weakly supervised learning
open-set noisy labels
Out-of-Distribution detection
deep learning

Abstract: Learning with noisy labels is a practically challenging problem in weakly supervised learning. In the existing literature, open-set noises are always considered to be poisonous for generalization, similar to closed-set noises. In this paper, we empirically show that open-set noisy labels can be non-toxic and even benefit the robustness against inherent noisy labels. Inspired by the observations, we propose a simple yet effective regularization by introducing Open-set samples with Dynamic Noisy Labels (ODNL) into training. With ODNL, the extra capacity of the neural network can be largely consumed in a way that does not interfere with learning patterns from clean data. Through the lens of SGD noise, we show that the noises induced by our method are random-direction, conflict-free and biased, which may help the model converge to a flat minimum with superior stability and enforce the model to produce conservative predictions on Out-of-Distribution instances. Extensive experimental results on benchmark datasets with various types of noisy labels demonstrate that the proposed method not only enhances the performance of many existing robust algorithms but also achieves significant improvement on Out-of-Distribution detection tasks even in the label noise setting.


Open-set Label Noise Can Improve Robustness Against Inherent Label Noise
Hongxin Wei! Lue Tao Renchunzi Xie Bo An
Schooo of Computer Science and Engineering, Nanyang Technological University, Singapore
2 College of Computer Science and Technology.
Nanjing University of Aeronautics and Astronautics, China 3 MIIT key Laboratory of Pattern Analysis and Machine Intelligence, China boan@ntu. edu.ag
[hongxin001 (iep:/m00xxxxix00000000 xier0002) ntu, edu. ag tlmi clmichael@ne @uuaa, edu, cn
Abstract
Learning with noisy labels is practically challenging problem in weakly super- vised learning. In the existing literature, open-set noises are always considered to be poisonous for generalization, similar closed set noises. In this paper, we empirically show that open-set noisy labels can non-toxic and even benefit the robustness against inherent noisy labels. Inspired by the observations, simple yet effective regularization by introducing Open-set samples with Dynamic Noisy Labels (ODNL) into training. With ODNL,, the extra capacity of the neural network can be largely consumed in way that does not interfere with learning patterns from clean data. Through the lens of SGD noise, we show that the noises induced by our method are random-direction, conflict-free and biased, which may help the model converge to flat minimum with superior stability and enforce the model to produce conservative predictions on Out-of-Distribution instances. Extensive experimental results on benchmark datasets with various types of noisy labels demonstrate that the proposed method not only enhances the performance of many existing robust algorithms but also achieves significant improvement on detection tasks even in the label noise setting.
propose
Introduction
The success of deep neural networks (DNNs) heavily relies on a large number of training instances with fully accurate labels [16]. In real-world applications, it is expensive and time-consuming to collect such large-scale datasets. To alleviate this problem, some surrogate methods are commonly used to improve labelling efficiency. such as online queries [3] and crowdsourcing [74]. These cheap but imperfect methods usually suffer from unavoidable noisy labels that can be easily memorized by Deep Neural Networks (DNNs), leading poor generalization performance [2, 79]. Therefore, designing robust algorithms against noisy labels is great practical importance.
Label noise can be separated into two categories: 1) closed-set noise, where instances with noisy labels have true class labels within the noisy label set [15, 24, 50, 51, 60, 63). 2) open-set noise, where instances with noisy labels have some true class labels outside the noisy label set [30, 54, 61, 67]. In the existing literature, open-set set noises are always considered to be harmful to the training of DNNs like closed-set noises [30, 61, 54, 67]. For example, ILON [61] reduces the effect of open-set noises by using noisy label detector and contrastive loss to pull away noisy samples from clean samples. Extended T [67] uses extended transition matrix mitigate the impacts of open-set noises and EvidentialMix [54] filters out the open-set examples by modelling the loss distribution. Therefore. there arises question to be answered: Do open-set noises always play negative role in the training?
"Coresponning author.
35th Conference on Neural Information Processing Systems (NcurIPS 2021).

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In this work, we answer this question by experiments with an open-set auxiliary dataset. We empirically show that additional open-set noises can be harmless to generalization the number of open-set noises is sufficiently large. More surprisingly, we observe that the open-set noises can even benefit the robustness of neural networks against noisy labels from the original training dataset, while the closed-set counterpart could not. Based on the "insufficient capacity" hypothesis [2], an intuitive explanation for this phenomenon is that increasing the number of open-set auxiliary samples slows down the fitting of inherent noises. In other words, fitting open-set label noises consumes extra capacity of the network, thereby reducing the memorization of inherent noisy labels. The details of experimental results are presented in Subsection 2.2.
Inspired by the above observations, we propose an effective and complementary method to improve robustness against noisy labels. The high-level idea is to introduce Open-set -et samples with Dynamic Noisy Labels (ODNL.) into training. each epoch, we assign random labels to instances of open-set auxiliary dataset by uniformly sampling from the label set. In other words, the label of each open-set sample is independently random and is constantly changing over training epochs. In this manner, ODNL consistently produce "benign hard examples" to consume the extra representation capacity of neural networks, thereby preventing the neural network from overfitting inherent noisy labels.
To further understand the effect of ODNL, we provide an analysis from the lens of SGD noise [7, 65], and formalize the differences and connections between our regularization and related methods. From the analysis, we show that the SGD noises induced by our method are 1) random random-direction, helping the model converge 3) biased, enforcing the model to produce more conservative predictions on OOD examples. In this manner, our method not only improves the robustness against noisy labels, but also provides benefits for OOD detection performance, which is also essential for the deployment of deep learning in safety-critical applications.
flat minimum: 2) conflict-free, leading good stability for the training;
To the best of our knowledge, we are the first to explore the benefits of open-set auxiliary dataset in learning with noisy labels. To verify the efficacy of our regularization, we conduct extensive experiments on both simulated and real-world noisy datasets, including CIFAR-10. 10, CIFAR-100 and Clothing datasets. In summary. the proposed method can be easily adapted for a wide range of existing algorithms to prevent overfitting noisy labels. Furthermore, experimental results validate that our regularization could also achieve impressive performance for detecting OOD examples, even the labels of training dataset are noisy.
2 Problem setting and motivation
In this section, first briefly introduce the problem setting of learning from training data with noisy labels. Then we start in Subsection 2.2 with an observational study highlighting the effect of open-set noisy examples from the auxiliary dataset on generalization and robustness against noisy labels.
2.1 Preliminaries: learning with inherent noisy labels
In this work, we consider multi-class classification with & classes. Let X C Rdd the feature space and y = (1,..... (1...., k) be the label space, we suppose the training dataset with N samples is given as {ar,, V: 1 where æ; E X' the i-th instance sampled from a certain data-generating distribution Durain in an i..d. manner and y4 fl...., . represents its observed label that might be different from the ground truth. To prevent confusion, the noisy labels in the training dataset are referred to as inherent noisy labels. A classifier is a function that maps the feature space to the label space f: : X + R²* with trainable parameter E IRP. In this paper, we consider the common case where the function f is DNN with the softmax output layer. The objective function of standard training can be represented as a cross- loss:
N
LOCE = EDr.ann (f ().)) ~ N
Σ
th log f (æ,;; 0),
(1)
where f (x;; 0) denotes the output of the classifier f with 0 for æ; and e = (0, (0..... 0)T {0, 13* is one-hot vector and only the y/-th entry of e is 1.
In addition to the training data, we consider there is an unlabelled auxiliary dataset consisting of open-set instances {3. J.M Specifically, open-set instances sampled from Dout, disjoint from

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100
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Stasire Cossiel
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Acourscy
2275
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2.70
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22455
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1.50
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1.0
230
250
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320 125 130
135 140
190 155
(a) Learning with Clean data
(b) Learning with inherent noiscs
(c) Losses of inherent noises
Figure 1: Average test accuracy (%) on CIFAR-10 with (a) clean labels (b) inherent noisy labels under different auxiliary dataset sizes. We use "open-set" and "closed-set" to denote two types of auxiliary datasets. The results of standard training are also reported for comparison. All experiments are repeated five times and the standard deviations are presented as error bars. (c) Training losses of inherent noises around the 40th epoch under different auxiliary dataset sizes (K).
Dirain Therefore, they are also called easy to get such an auxiliary dataset, which is commonly used in OOD detection tasks [20, 48].
(OOD) instances. In real applications, is
2.2 Motivation: the role of open-set label noise in generalization and robustness
To analyze the effects of open-set noisy labels, we build an open-set noisy dataset with the unlabelled auxiliary dataset. For each open-set instance, we assign a fixed random label jí uniformly sampled from the label set {1....,A }.. In each iteration, we concatenate the two batches that are sampled from the training dataset and the open-set noisy dataset respectively, then perform standard training. In addition, we conduct controlled experiments by replacing the open-set dataset with a closed-set dataset to verify the effectiveness of open-set noisy dataset during the training. To demonstrate the importance of the sample size of the given auxiliary dataset, compare the performance of training with different-sizii auxiliary datasets, built by randomly sampling from the original auxiliary dataset.
Throughout this subsection, we perform training with WRN-40-2 [77] on CIFAR-10 10 [27] using standard cross-entropy loss. For experiments under inherent label noise, we use symmetric noise with 40% noisy rate. For auxiliary dataset, we use 300K Random Images² [20] as the open-set get auxiliary dataset and CIFAR-5m' [21, 43] as the closed-set auxiliary dataset. Specifically, all images that belong to CIFAR classes are removed in 300K Random Images so that Dtrain and Dout are disjoint. More training details can be found in Appendix B.
Learning with clean labels. Fig. 1a presents the results on clean training set of CIFAR-10, training with the open/closed-set noisy auxiliary dataset. The results show that open-set noisy labels from the auxiliary dataset become harmless gradually with the increase of the sample size of the auxiliary dataset, while closed-set noisy labels consistently deteriorate the generalization performance. Further- more, we observe that closed-set noisy labels result in unstable training shown by the large standard deviations the figure. It worthy note that open-set noisy labels do not encounter this issue.
Learning with inherent noisy labels. Fig. lb presents the results on CIFAR-10 10 with inherent noisy labels, training with the open/closed-set noisy auxiliary dataset. Surprisingly, we can observe that as we increase the sample size of the open-set noisy dataset, the open-set noises can even improve the generalization performance beyond standard training without the noisy auxiliary dataset. In this case, closed-set noisy auxiliary dataset still plays a negative role in the training.
An intuitive interpretation. From the results, the auxiliary dataset would be harmless and can even benefit the robustness against inherent noisy labels, if the sample size of the auxiliary dataset is large enough. This phenomenon is somewhat counter-intuitive since one would expect that open-set noisy labels should have been "poison" to the training of network, like closed-set set noises [30, 54, 61, 67]. Here we provide an intuitive interpretation from "insufficient capacity" [2]: increasing the number of examples while keeping representation capacity fixed would increase the time needed to memorize the data set. Hence, the larger the size of auxiliary dataset is, the more time it needs to memorize the open-set noises in the auxiliary dataset as
can hypothesize that open-set noisy labels in
The dataset published on https: httpehh....cmeeeeddde/////iiieee exppouu..........r.eeu.e...
3 The dataset is published on aattp:://gitee com conm/peetum/ciffee cifaróm.
3

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well as the inherent noises in the training set, relative to clean data. This interpretation is supported by our plot of training losses of inherent noises with different-sized auxiliary datasets in Fig. Ic. where increasing the number of open-set auxiliary samples slows down the fitting of inherent noises.
3 Method: Open-set regularization with Dynamic Noisy Labels
Motivated by the previous observations, we propose to utilize open-set auxiliary dataset to further improve the robustness against inherent noisy labels. This approach can be considered an auxiliary task that continuously consumes the cxtra capacity neural networks but does not interfere with learning patterns from clean data. Such auxiliary tasks will prevent deep networks from overfitting noisy data.
Dynamic Noisy Labels. To maintain the effectiveness of the auxiliary task during training, an ideal method is to use an auxiliary dataset with unlimited instances based on "insufficient capacity" [2]. In this way, the network would always try to fit a stream of new instances sequentially in an online learning setting. However, it is unrealistic to provide such an auxiliary dataset in real scenarios.
To relieve the requirement on sample size, we propose an alternative approach called Dynamic Noisy Labels. For each open-set instance E2 noisy label y uniformly sampled from the label set (1....,k) k} independently, regardless of the instance á;, More importantly, the generated labels are not fixed and would be changed dynamically over training epochs. In this way. the auxiliary task becomes an "impossible mission". continuously consuming the extra capacity of network. Without Dynamic Noisy Labels, the effectiveness of the auxiliary task would be degraded gradually during training, because deep network can easily memorize the finite open-set samples, even though the labels of those samples are randomly generated [2, 79].
Training Objective. For the original training dataset, we use the standard cross entropy as the training objective function:
Li Ep (f 0), v)] ED [-e" log (x;; 0)],
(2)
For the auxiliary dataset, we enforce the outputs to be consistent with dynamic noise labels, uniformly sampled from the label set in each epoch. Therefore, the training objective function as follows:
L = ED... (e 0). D... [-ee log (ā:; 0)] where ī ~ Ux
where Uu represents a discrete uniform distribution on the label set [l,...,k}..
Combining the original training dataset and the auxiliary dataset in the training, now we can formalize ODNL minimizing the final objective:
Liotal = L1 + r7 L2 = ED (f(x;; v)l + ED.... (f(=:)) 0), 3)]
(4)
where 17 denotes the trade-off hyperparameter. The details of ODNL are provided in Algorithm 1.
Extensions to Other Robust Training Algorithms. It is worthy to note that our regularization is general method and can be easily incorporated into existing robust training algorithms, including sample selection[15, 63], loss correction [50], robust loss function [80, 62), etc. Given the original learning objective Crobust of the robust methods and the hyperparameter 7), we can formalize the final objective as:
Liotal
Crobust + 11 L2
(5)
4 Understanding from SGD noise
To further understand the effeet of our regularization, we provide an analysis from the lens of SGD noise. Following SLN [7], here we consider an original training sample (æ, y) and an open-set sample ã at each SGD step for convenience. In parameter updates, regularization adds noises to the original gradients with the open-set noisy sample 전 and dynamic noisy label ÿ ~ Ue:
Voliotal Vol(f(x; 0), v) + (=)(f(2;0).8)
(6)

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Algorithm 1 Open-set Regularization with Dynamic Noisy Labels
Input: Training dataset Dirain Open-set auxiliary dataset Douti
1: for each iteration do
2: Sample bini-batch of original training data [T; 34) Fo= from Drainn
3: Sample mini-batch of open-set data { from Dout
4: Generate random noisy label Vr ~ U4 for each open-set data ã,
5: Perform gradient descent on f with Liotal from Equation (4);
6: end for
where denotes the cross entropy loss for single sample, For convenience, we omit the hyperparameter y) and use f, f to indicate the model output (ar; 0) for the original training sample and the model output f (æ;; 0) for the open-set noisy sample. Following the standard notation of the Jacobian matrix, we have Vol RIxb Vyl E IR RIxk Vof E RR Ve.f f IRt and Vof; RIXP, The proofs of the following propositions are presented in Appendix A.
Proposition 1. For the cross-entropy loss, Eq. (6) induces noise 2 Vof
f, on Vol(S,y). s.l.
E RR, j ~ Us, where f, denotes the element-wise division. Note that the expected value of noise on the i-th parameter 0; is - - f,
Ve,f.
The effects SGD noise. From Proposition 1, we find that our regularization induces SGD noise sampled from a uniform distribution over {- fof; / 1 The effects of the noises can be analyzed from three aspects:
Firstly, our regularization yields SGD noises with random direction, uniformly sampled from the gradients of the open-set noisy sample. In this way, the SGD noises make the training difficult to converge and help escape local minima [4, 22, 25, 26, 46]. Without the noises, the model would quickly converge to a local minimum because the optimization of parameters always follows the direction of gradient descent during the training.
Secondly, the noises are independently random to the gradients Vel(f. y) from the original training example as the noises are conducted with the open-set sample æ from Dout- If closed-set samples are used here, the generated noises might be conflicted with the original gradients, leading to unstable training and limiting the improvement from noises. This argument clearly supported by the experimental results in Fig. I and Fig. 3a.
Finally, the expected value of noise is not zero so the noise is biased. In particular, the expected value of noise enforces the model f to produce more conservative predictions ciit-of-Disribuutiin samples. Consequently, this feature would lead to performance improvement on OOD detection tasks as shown in Subsection 5.4.
To further demonstrate the importance of open-set samples in the auxiliary dataset, we conduct experiments with synthetic auxiliary datasets consisting of convex combinations x' of open-set samples E and closed-set samples with hyperparameter a: x' (1 a) . + a x. Here, use 300K Random Images [20] and CIFAR-5m [21, 43] as the open-set and closed-set dataset. More training details can be found in Appendix B. The results in Fig. 3a show that using open-set auxiliary dataset achieves better performance on robustness against inherent noises.
Relations to label randomization methods. Label randomization is also applied in some existing work the context of deep Icarning[7, 36, 71]. For example, DisturbLabel [71] intentionally generates incorrect labels small fraction of training data to improve generalization performance under the settings of clean labels, interpreted as an implicit model ensemble. It can be seen as using a part of training data closed-set auxiliary dataset. In such way, DisturbLabel can be interpreted as a special case of our method with a closed-set auxiliary dataset.
To mitigate the issue of label noise, Stochastic Label Noise (SLN) [7] introduces a variant of SGD noise induced by adding dynamic and mean-zero perturbations on the labels of training dataset, while Label Smoothing [36] uses fixed and biased perturbation the label. The noise gradients induced
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(a) Standard
(b) OE
(c) SLN
(d) Ours
Figure Loss landseapes around the local minimum of models trained on CIFAR-I0 with symmetric- 40% noisy labels. We compare their sharpness with the same-scale z-axis and show the loss distribu- tion by drawing color bars separately. We show that our method and SINN lead to flat minimum, while the models trained by Standard and OE converge sharp minimum.
Stanlari
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Tel
65
5ym
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Natey Openaer
Depandent
Type
(a) The importance of open set
(b) Our method can improve SLN
(c) Compare with OE.
Figure (a) Test accuracy (%) of ODNL (n) 1) on CIFAR-10 with symmetric-409z inherent noisy labels using different synthetic auxiliary datasets: the larger the a is, the closer the auxiliary sample distribution Dout is to the origin sample distribution Duain- (b) Comparison of performances on CIFAR-10 with different types of inherent noisy labels using SLN and SLN+Ours. Here follow the settings of SLN and the training details are provided in Appendix B. (c) Comparison of performances CIFAR10 with different types of inherent noisy labels using OE and Ours.
by SLN are given as follows:
vol(s,)) Vol e" + oz22))
where oy > 0, E R² N (xx))
Proposition 2 (Proposition 3 in Chen et al. [7]). For the cross-entropy loss, Eq. (7) in SLN induces noise -NO,, M) on Vol(f.W) v) s.l, M TRxxP and M (I.....p), where J, denotes the element-wit wise division.
Vo.f T Vo
Vi,j
From Proposition 2, we show that SLN induces SGD noises that obey a Gaussian distribution, where the standard deviation can be adjusted by the hyperparameter øy. Therefore, SLN and ODNL can be considered as different variants of SGD noise, helping the network converge to a flat minimum, which is expected to provide better generalization performance [4, 22, 25, 26, 46]. The effects explicitly presented in Fig. 2c and Fig. 2d. Moreover, the results in Fig. 3b illustrate that our method could further improve the performance of SLN* under various types of label noise, showing the complementarity between SLN and our method.
Relations to Outlier Exposure. Outlier Exposure (OE) [20] is effective method designed for OOD detection tasks. Specifically, OF also makes use of a large, unlabeled auxiliary dataset sampled from Dout. and regularizes the softmax probabilities be uniform distribution for out-of- distribution data in the auxiliary dataset. In such manner, OE train the model discover signals and leamn heuristics to detect whether query is sampled from Derain Dout. The training objective is to
minimize: ((..)) [e(f(x; 0), v)l + A EnwDon 1 Σt log ft (Ø: 0)
Proposition 3. For the cross-entropy loss, each open-set sample in OE induces bias ( v), s.t. RP, where j. denotes the element wise division.
1 Σド Vol, on Vol(f.
f,
WWe use the official implementation: https /i github. com/ chenpf1025/SS.

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Table 1: Average test aceuracy (%) with standard deviation CIFAR-10 under various types of noisy labels (over trials). The bold indicates the improved results by integrating our regularization.
Method
Sym-20%
Sym-50%
Asymmetric
Dependent
Open-set
Standard
86.93++0.49
69.48-+0558
0..3440.96
68.39+00.65
88.45+0.55
+ ODNL (Ours)
91.06++0.64
82.50-00.58
90.00++0.35
85.37+0.32
91.47++0.39
Decoupling
88.31=0.39
80.81=0.85
84.85+00.2
82.44=0.54
84.69+0.46
ODNL (Ours)
89.70 0.74
81.48=00.37
87.36-+0.68
84.11=0.51
86.22::0.57
F-correction
84.95=00.27
72.11=0.115
84.46-10.6
72.44=0.58
85.54:10.86
+ ODNL (Ours)
88.99-0023
82.42-+0.29
88.50+0.155
84.70=00.99
88.92+0.65
PHuber-CE
90.92-10.28
74.07-10.49
81.26:10.65
7..07
88.35:10.21
ODNL (Ours)
92.05==0.36
82.63=0.28
88.24:10.43
84.11=0.445
90.85=
Co-teaching + +ODNL (Ours)
91.72=+0.35
86.65-00:43
87.56:10.36
88,6420.79
89.91
93.07-10.79
89.79110.76
88.4810.37
90.99=0.228
91.8810.53
JoCoR (A- = 0.5)
92.68-10.33
88,36110.64
84.06:10..37
90.1110.19
88.80.10.54
+DNL (Ours)
93.65
91.17:10.52
88.19:10.69
92.09 0.84
01.73
Comparing Proposition 3 with Proposition 1, our method should have similar regularization effect to OE on OOD examples, because the expectation of the SGD noises from ODNL is equal to the bias induced by OF However, OE cannot improve the robustness against noisy labels as it does not induce dynamic noises so that the optimization of parameters always follows the direction of gradient descent. The difference empirically shown in Fig. 3c and we further verify it in Subsection 5.4.
5 Experiments
In this section, we verify the effectiveness of our method on three benchmark datasets: CIFAR-10/100 and ClothingIM. M. We show that ODNL could not only significantly improve the robustness against various types of inherent noisy labels in standard training, but also enhance the performance of existing robust training techniques. Then we perform a sensitivity analysis to validate the effect of r). Finally, we conduct experiments to evaluate the performance of ODNL on OOD detection task.
5.1 Setups
We comprehensively verify the utility of ODNL' on different types of label noise, including symmetric noise, asymmetric noise[80], instance-dependent noise [6] and open-set noise [61] synthesized on CIFAR-10/100 and real-world noise on Clothing IM [70]. Symmetric noise assumes each label has the same probability of flipping to any other classes. We uniformly flip the label to other classes with an overall probability 20% and 50%. Asymmetric noise assumes labels might be only flipped to similar classes [7, 50, 80]. Noisy labels are generated by mapping TRUCK + AUTOMOBILE, BIRD > AIRPLANE, DEER - HORSE, and CAT < DOG with probability 40% for CIFAR-1O. For CIFAR- 100, we flip each class into the next circularly with probability 40%. Instance-dependent noise assumes the mislabeling probability is dependent on each instance's 's input features [6. 7, 68). We use the instance-dependen noise from PDN [68] with a noisy rate 40%, where the noise is synthesized based on the DNN prediction error. Open-set set noise contains samples that do not belong to the label set considered in the classification task [61]. We generate open-set noises CIFAR-10 by randomly replacing 40% of its training images with images from CIFAR-100.
We perform training with WRN-40-2 [77] on CIFAR- 10 and CIFAR- 100. The network is trained for 200 epochs using SGD with a momentum of 0.9, weight decay of 0.0005. each iteration, both the batch sizes of the original dataset and the open-set auxiliary dataset are set as 128. We set the initial learning rate as 0. 1, and reduce by a factor of 10 after 80 and 140 epochs. We use 5k noisy samples as the validation to tune the hyperparameter 7) in {0.1, 0.5. 1, 2.5, 5). then train the model on the full training set and report the average test accuracy in the last 5 epochs. All experiments are repeated five times with different seeds and more training details can be found in Appendix B. We use 300K Random Images [20] as the open-set auxiliary dataset and more discussions on the different choices of open-set auxiliary dataset are presented in Appendix C.
S 'Tee code is published on https: //gi thub. com/honggxin001/0DDLL
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Table 2: Average test accuracy (%) with standard deviation CIFAR-100 under various types of noisy labels (over trials). The bold indicates the improved results by integrating our regularization.
Method
Sym-20%
Sym-50%
Asymmetric
Dependent
Standard
64.87=0.81
48.55=0.65
48.77=0.86
53.94+00.52 62.45 + 0.41
+ ODNL (Ours)
68.82
54.08 0.554
58.61 + 0.25
Decoupling
64.26:#0.23
49.09=0.81
49.92-0.71
55.17=0.34
+ ODNL (Ours)
66.86=10.35
51.89=0.47
57.47=0.49
58.52:
F-correction
62.05=0.335
52.27+0.115
54.45
57.47+
+ ODNL, (Ours)
65.53 0.28
57.12+0.66
57.89 0.55
60.95 0.81
PHuber-CE
01.27=0.74
61.37+0.28
48.1440..3
64.80=#..22
+ ODNL (Ours)
72.27-00.53
63.33+00.48
53.631.072
66.5410.60
Co-teaching
72.35+0.60
65.21+0.88
54.16+0.17
67.85+0.82
+ +DDNL (Ours)
+.22
65.46
54.92 0.31
68.37 0.12
JoCoR (A = 0.5)
03.4440.19
67.53+0.79
57.02++..2
70.01 71.28
+ ODNL (Ours)
0..16
68.07+0.47
58.44410.34
Table 3: Test accuracy (%) on Clothing IM with pretrained ResNet-50. All experiments imple- mented based on DivideMix's official implementation. The bold indicates the improved results.
Method
Standard
Forward Co-teaching ODNL (Ours)
SLN
ODNL (Ours) DivideMix ODNI. (Ours)
best
70.17
70.46
71,32
72.47
72.31
73.12
74.32
74.89
last
68.23
69.34
71.02
72.38
71.94
72.98
73.83
74.35
5.2 CIFAR-10 and CIFAR-100
On CIFAR-10 and CIFAR-100, we verify that ODNL can boost existing robust training methods by integrating ODNL with the following methods: Decoupling [40], which updates the parameters only using instances that have different predictions from two classifiers. 2) F-correction [50], which corrects the prediction by the label transition matrix. As the setting in F-correction, we first train standard network to estimate the transition matrix Q 3) PHuber- CE [41], which uses composite loss-based gradient clipping, a variation of standard gradient clipping for label noise robustness. 4) Co-teaching [15], which trains two networks simultaneously and cross-updates parameters of peer networks. 5) JoCoR [63], which trains two networks simultaneously and reduces the divergence between the two classifiers by explicit regularization.
The average test accuracy with WRN-40-2 [77] are re- ported in Table and Table 2. The results show that in- corporating our regularization into existing robust training methods consistently improves their performance against various types of noisy labels. In particular, we can observe that our method can boost almost all types of existing methods, including robust loss function, sample selection, loss correction, etc., showing that our regularization based upon an open-set auxiliary dataset is a complementary method to existing robust algorithms. More intriguingly, while most existing methods do not bring benefits to the robustness against open-set noisy labels in the training set of CIFAR- 10, our regularization could still enhance the performance of all existing methods in this case.
Test Accuracy (9)
0.1
1.5
100
125
352
250
Epoch
Figure 4: Results of sensitivity analysis on CIFAR-I0 with different r7.
To further illustrate the influence of 17, we present sensi- tivity analysis on CIFAR-10 10 with symmetric-40F noisy labels in Fig. 4. Specifically, we highlight the differences in the trend test accuracy after the second decay of learning rate at the 140th epoch. We can observe that with a large 1), the decreasing trend of test accuracy caused by inherent noisy labels nearly eliminated. The result verifies that our regularization is an effective method to reduce the overfitting of inherent noisy labels.
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Table 4: OOD detection performance comparison CIFAR-10 with symmetric-40% noisy labels. All values are percentages and are averaged over the ten test datasets described in Appendix B. 1 indicates larger values are better, and + indicates smaller values better. Bold numbers superior results. Detailed results for each OOD test dataset can be found in Appendix C
Method
Test Accuracy
FPR95
AUROC 1
AUPR
MSP
77.87
92.01
54.64
19.41
MSP+OE MSP+Ours
78.12
24.91
94.24
78.09
86.29
13.43
96.67
84.70
5.3 Clothing
Clothing M [70] is a large-scale dataset with real-world noisy labels of clothing images. There are million noisy samples for training. 14K and 10K clean examples for validation and test respectively. Following DivideMix [33], we conduct experiments by sampling a class-balanced training subset in each epoch and use ResNet-50 with ImageNet pretrained weights. We set the 1) of the vanilla ODNL as For the auxiliary dataset in our method, we use ImageNet 2012 dataset [53] and remove all classes related to clothes. Other training details strictly follow the settings of DivideMix [33], presented in Appendix B. As shown in Table 3, best denotes the score of the epoch where the validation accuracy optimal, and last denotes the scores at the last epoch. The vanilla ODNL outperforms many simple baselines, and it can further improve the performance of SLN and DivideMix. The results show that method is applicable for large-scale real-world scenarios.
5.4 OOD detection with noisy labels
Out-of-distribution (OOD) detection is an essential problem for the deployment of deep learning especially in safety-critical applications [20, 35]. From the analysis in Section 4, we know that our regularization should have a similar regularization effect to OE [20]. which enforces the model to give conservative outputs on OOD examples. Here, we conduct experiments on CIFAR-10 with symmetric-40%5 noisy labels to verify the advantage of our method on OOD detection tasks even in a weakly supervised setting. We expect the trained models could perform well in the OOD detection task, even if the labels of training dataset are noisy. Experiments on CIFAR- 10 with clean labels are also conducted and the results can be found in Appendix C. The training settings are the same those in subsection 5.2. Following OE [20], we consider the maximum softmax probability (MSP) baseline [18], which gives the maximum of softmax output the OOD score. For the OOD test datasets, we use three types of noises and seven common benchmark datasets: Gaussian, Rademacher [20], Blobs [20]. Textures [9]. SVHN [45], 100, Places365 [82], LSUN-Crup [75], LLUU--SSSS [75], and iSUN [72]. We measure the following metrics: (1) the false positive rate (FPR95) of OOD examples when the true positive rate of in-distribution examples is at 95%; (2) the area under the receiver operating characteristic curve (AUROC); and (3) the area under the precision-recal curve (AUPR). Table 4 presents the test accuracy and the average performance over the three types of noises and seven OOD test datasets. We can observe that our method achieves impressive improvement on both the test accuracy and the detection performance, while OE does not bring benefit on the generalization performance. The results can further serve as evidence for the analysis in Section 4.
Related literature
Learning with noisy labels. Learning with noisy labels is vital topic in wcakly-supervised learning. attracting much recent interest with several directions. 1) Some methods propose to train on selected samples, using small-loss selection [15, 63, 76), GMM distribution 33] or (dis)agreement between two models [40, 63, 76]. 2) Some methods aim to design sample weighting schemes that give higher weights on clean samples [24, 34, 52, 55, 64]. 3) Loss correction is also popular direction based on an estimated noise transition matrix [19, 50] or the model's 's predictions [1, 6, 51, 58, 81]. 4) Robust loss functions are also studied to have theoretical guarantee [11, 12, 37, 38, 62, 73, 80]. 5) Some method apply regularization techniques to improve generalization under the settings of label noise
[10, 23, 66), like gradient clipping [41], label smoothing [36, 57], temporal ensembling [28] and
virtual adversarial training [42]. 6) Some training strategics for combating noisy labels are built based

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upon semi-superrised learning methods [33, 47]. Studying robustness against label noise helps us further understand the essence of training deep neural networks.
Utilizing auxiliary dataset. To the best of our knowledge, we are the first to explore the benefits of open-set auxiliary dataset in learning with noisy labels. Auxiliary dataset is also utilized in other problem settings of deep learning. For example, pre- pre-training a network on the large ImageNet database [53] can produce general representations that are useful in many fine-tuning applications [78]. Representation learned from images scraped from search engines and photo-sharing websites could improve objective detection performance [8. 39]. OE uses an auxiliary dataset teach the network better representations for OOD detection [20]. To improve robustness against adversarial attack, a popular method is to train on adversarial examples which can be seen as a generated auxiliary dataset [14]. Unlabelled data is also shown to be beneficial for the adversarial robustness [S. 59]. Note that the unlabelled data for improving adversarial robustness is required to be in-distribution, our method utilizes aut-of-distributio instances to improve robustness against noisy labels. Recently, OAT [31] designed to use -iitt---iitiibttion data for improving robustness against adversarial examples, a detailed comparison between OAT and our work is provided in Appendix D.
OOD detection. OOD detection is an essential building block for safely deploying machine learning models in the open world [18, 20, 35]. A common baseline for OOD detection is the softmax confidence score [18]. It has been theoretically shown that neural networks with ReLU activation can produce arbitrarily high softmax confidence for OOD inputs [17]. To improve the performance, previous methods have explored using artificially synthesized data from GANs [13] or unlabeled data [20, 29] as auxiliary OOD training data. Energy scores are shown to be better for distinguishing in- and outto--distribution samples than softmax scores [35]. As a side effect, our method could also achieve superior performance in OOD detection tasks even if the labels of training dataset are noisy.
7 Conclusion and Discussion
In this paper, we proposed Open-set regularization with Dynamic Noisy Labels (ODNL), simple yet effective technique that enhances many existing robust training methods to mitigate the issues of inherent noisy labels across various settings. To the best of our knowledge, first to utilize open-set auxiliary dataset in the problem of noisy labels. We show that ODNL can be considered variant of SGD noises that usually leads to flat minimum. Extensive experiments show that ODNL can help improve the robustness against various types of noisy labels and it is applicable for large-scale real-world scenarios. Moreover, ODNL also brings "side effect": it can improve the OOD detection performance, which is also essential for the deployment of deep learning in safety-critical applications. On the other hand, ODNL is computationally inexpensive and can be easily integrated into existing algorithms. Overall, our method is an effective and complementary approach for boosting robustness against inherent noisy labels.
method is the
In the future, the observations and analyses in this work can help understand the effect of open-set noises and inspire more specially designed methods using auxiliary dataset to combat label noise in deep learning. There are still some limitations in our current exploration and method as follows.
instance instance-dependent open-set noisy labels. In this work, we only consider open-set noisy labels that are independent from the instances. In real scenarios, an open-set sample with a noisy label might be similar to the clean samples in this class but still do not belong to this class. We may get different observations when we extend the setting to instance-dependent open-set noisy labels.
Synthesized auxiliary dataset. In this work, we use realistic open-set dataset in our method. It is also natural to ask whether synthesized data can be used combating noisy labels. For example, one may generate adversarial examples with dynamic noisy labels to improve robustness against noisy labels.
Acknowledgments and Disclosure of Funding
This research was supported by the National Research Foundation, Singapore under its Al Singapore Programme (AISG Award No: A1SG-RP-2019-00113) National Satellite of Excellence in Trustworthy Software Systems (Award No: NSOE-TSS2011900 and NTU.
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15


Reviews:
[{"review_id": "sEyGOCFII0Q", "reviewer": "Reviewer_aFDj", "summary": "This work proposes augmenting a training set by using an out of distribution auxiliary dataset to help train a robust classifier through randomly sampling labels for the OOD dataset. Model's trained with this additional data perform better than related work on a variety of noisy classification tasks, including out of distribution detection.", "questions": "", "limitations": "", "rating": 7, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "Strengths:\n\n-This proposed method is straightforward and effective\n\n-A thorough analysis is done as to how ODNL effects models' parameters during training, specifically comparing to the most similar method Outlier Exposure.\n\n-Many different experiments are used to demonstrate the effectiveness of ODNL.\n\n\nOriginality: \n-The proposed method is fairly simple, that is, modifying Outlier Exposure to use random 1hot labels rather than the uniform distribution over all labels.\n\nCorrectness: \n-Section 3 and 4 are well written and thoroughly analyze the effects of the proposed method.\n\nClarity: \n-The paper is well written and easy to follow\n\nRelation to prior work: \n-This work appropriately places itself within the recent literature on noisy label classification.\n\nReproducibility:\n-The source code is provided and the method seems easy to apply to any classifier. However, the used auxiliary dataset is no longer publicly available.\n\n", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "Weaknesses:\n-The only weakness is the lack of novelty. This is a simple, yet effective, change to OE. And while a detailed analysis is performed, I would love to see the outlier dataset used more than just for crossentropy loss.\n\nThe authors do not address negative societal impact of their work. It could be mentioned how additional data increases compute/energy usage.\n\nAfter seeing the author's response, and the rest of the discussion, I think the discovery that openset instance improve noisy label classification is definitely of interest to the community.  I've increased my score to reflect such.", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "3"}, "extra_text": {}}, {"review_id": "pMluVTU-31E", "reviewer": "Reviewer_5cBL", "summary": "This paper proposed a training method for improving the robustness of deep neural networks against inherent label noise. Specifically, the authors first found that the open-set noise is helpful for the generalization and robustness of a deep neural network, and then proposed the strategy that use an additional training set with open-set noise as the regularization in the training objective. Experiments showed improved performance by the proposed method against inherent label noise.", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "The counter-intuitive discovery that the open-set is not harmful but even helpful for the robustness of neural networks is interesting, and worth to be paid more attention to. Based on this, the proposed robust training method is simple to implement yet effective.\n\nHowever, there still exist some issues to be addressed:\n\n1. Theoretical justification. The authors provided theoretical analysis for the effectiveness of the proposed method from the perspective of SGD noise. However, it seems that this is not that essential, since there can be simpler method to add SGD noise other than introduce the additional data with open-set noise. The authors also explained the effectiveness by claiming that the capacity of the neural networks can be first consumed by the open-set noise and then the inherent noise. However, there is no theoretical guarantee for this.\n\n2. Implementation issue. The proposed method introduces two additional hyper-parameters to be tuned, i.e, the size of the auxiliary dataset and $\\eta$. While these hyper-parameters can be tuned by validation as mentioned by the authors, it could increase the computational cost. Besides, as can be observed from Figure 4 can claimed by the authors, the decreasing trend of test accuracy caused by inherent noise can be avoided with a larger $\\eta$, which raises a question that what the performance could be if further increasing $\\eta$. For the size of the auxiliary dataset, as can be observed from Figure 1 of the appendix, it affects the performance with a relatively large extent, while with no consistent trend, which increases the difficulty in tuning it. The authors also claimed that the proposed method is computational inexpensive, which I do not quite agree, since enlarging the training set can definitely increase the computational complexity.\n\n3. Limited improvement on real world dataset. As can be seen from Table 3, the performance improvement solely by the proposed method is not that significant than solely by DivideMix. Considering the combination of the two methods, DivideMix improves the proposed method more than reverse.", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "The authors did not mention the limitations of their method. However, in my opinion, the major limitation is the practical implementation issue as mentioned in the \"Main Review\" part, i.e., additional hyper-parameters and extra computational cost.", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "8 hours."}, "extra_text": {}}, {"review_id": "esbaQVGyC6f", "reviewer": "Reviewer_wWfP", "summary": "This paper provides an interesting perspective on how to deal with open-set label noise when learning with noise labels. In the noise label community, open-set noises are usually considered to be harmful for generalization, similar to closed-set noises. However, in this paper, the authors present a new perspective that the open-set noise can help generalization against inherent label noises by using proposed Dynamic Noisy Labels (ODNL) regularization on auxiliary open-set samples. In addition, using analysis from the lens of SGD noise, they empirically show the superiority of ODNL over other related regularizations such as Stochastic Label Noise (SLN) and Outlier Exposure (OE).", "questions": "", "limitations": "", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "The main contribution of this paper is that it is the first work to explore the benefits of open-set noise in learning with noisy labels. However, the authors have missed a very important paper related to their main argument. \n\n[1] Lee, Saehyung, et al., \"Removing Undesirable Feature Contributions Using Out-of-Distribution Data.\", ICLR, 2021.\n\n[1] argues that the open-set noise (i.e., out-of-distribution) can help to improve the generalization of DNNs by using uniform-label regularization on the auxiliary open-set noise samples. I think this uniform-label regularization has basically the same effect for improving the generalization of DNNs with ODNL, except its dynamicity. \nAlthough [1] is not focusing on the label noise problem, I think the author should refer to the paper and clarify the difference and advantages against it. Also, the statement like “open-set noises are always considered to be harmful to the training of DNNs” should be modified.\n\nMeanwhile, the analysis through SGD noise and comparisons to other regularizations such as SLN and OE are clear. In figure 3, with the convex combination of open-set noise and closed-set noise, the importance of open-set noise is clearly shown.\nIn the experiment, as shown in Table 1, the proposed method has advantages in its applicability to various existing methods because of its simplicity. Also, it showed a significant performance boost combined with other baselines. The out-of-distribution detection performance was also enhanced by their proposed method.\n\nAlthough the authors provide insightful analysis and impactful methods that leveraging open-set noise to improve robustness against label noise, they missed out on an important related work that seems to have a very similar design and effect with the proposed one. In this concern, I recommend a score marginally below the acceptance threshold.\n", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "An important related work that has a similar design and effect to the proposed method is missing.\nThere is no detailed description for Figure 2; what is x- and y-axis, how much auxiliary open-set noise data is used.\nThere might be a notation error in line 161. $f(\\tilde{x}, \\theta)$ seems to be $\\tilde{f}(\\tilde{x}, \\theta)$.\n", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "6"}, "extra_text": {}}, {"review_id": "5dxC6KDKqdW", "reviewer": "Reviewer_z2XB", "summary": "This paper empirically shows that open-set noisy labels can benefit the robustness against inherent noisy labels in the training data, and proposes a simple but effective regularization method through introducing open-set data with Dynamic Noisy Labels (ODNL) into the training process. Specifically, the ODNL utilizes the open-set data with randomly generated labels in training, to exploit the extra capacity of neural networks, and thus makes the network avoid the memorization of noises in training data. The model trained with ODNL can achieve a better capability on Out-of-Distribution detection tasks. Extensive experiments show the effectiveness of the proposed method.", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "This paper proposed a method for learning with noisy labels called Open-set data with Dynamic Noisy Labels (ODNL). Specifically, the ODNL utilizes the open-set data with randomly generated labels in training, to consume the extra capacity of neural networks, and thus make networks avoid the memorization of noises in training data. The model trained with ODNL can also achieve a better capability on Out-of-Distribution detection tasks. Extensive experiments show the effectiveness of the proposed method.\n\nThis work is somewhat novel since it first introduces open-set auxiliary datasets in learning with noisy labels. It also provides comparisons with other methods which deal with learning with noisy labels like label randomization and outlier exposure.\nFor the quality of this paper, the whole process is technically sound. \n\nHowever, there are also some points that need to be improved. \n\nFirst, the selection of a closed-set dataset should be reconsidered. Since the 80 Million Tiny Images dataset is collected from the web while CIFAR-5m is generated through Denoising Diffusion Probabilistic Models (DDPM), the comparison of using closed-set or open-set dataset may be not sufficient enough to reach the conclusion because we cannot make sure that images generated form DDPM could have an identical distribution as real data. Secondly, it would be better if the hyperparameter \\eta is indicated in the experiment results. Thirdly, a curve of performance for models with different percentages of symmetric noise labels could better illustrate the improvement of the method. Finally, since an intuitive interpretation of this method is that it consumes insufficient capacity and makes the model hard to memorize the noises, a comparison experiment between using the proposed method on a small neural network, like ResNet-18, and a big neural network, like WRN, is quite meaningful. Here we can see that the improvement between standard method and standard method with ODNL in the paper (trained with WRN-40-2) is higher than those when trained with ResNet-34, which is shown in the appendix, under open-set and dependent noise, but lower under asymmetric noise.\n\nAnd for clarity, this paper is well organized and written clearly. It is a bit of a pity that Table 3 is not close enough to its corresponding context, but that is okay.\n\nAs for the significance, the proposed method is simple while the improvement reported in the paper is quite impressive. This method could be a new way for learning with noisy labels if the claim of the ineffectiveness of using a close-set dataset as an auxiliary dataset could be further supported.\n", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "The authors have addressed some limitations about the work, like instance-dependent open noisy labels and using a synthesized auxiliary dataset. However, more discussions or possible solutions about the limitation of their method should be listed out, such as: \n(1) The discussion of the ineffectiveness of using closed-set auxiliary data in training. Since the 80 Million Tiny Images dataset is collected from the web while CIFAR-5m is generated through Denoising Diffusion Probabilistic Models (DDPM), it is not sufficient enough to reach the conclusion. \n(2) The intuitive interpretation of consuming ‘insufficient capacity' could be further explained by using models with different depths and widths.\n", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "5"}, "extra_text": {}}]

Author rebuttal:

Meta-review: UPDATE: The revision from the authors has been reviewed.  After some back-and-forth with the authors to discuss the details of the 300K Random Images dataset that they have chosen to use, the paper has been officially accepted.

----

Reviewers generally appreciated the paper's observation regarding the benefit of open-set label noise as being interesting and non-obvious. The simplicity and effectiveness of the technique, coupled with the theoretical analysis, were also appreciated. Some concerns were however raised on:

(1) choice of hyper-parameters

(2) relation to OAT and other out-of-distribution regularization schemes

Point (1) was convincingly addressed in the response. There was some debate about point (2). From my reading, I do agree that there are conceptual similarities between the proposed technique and OAT. However, I also take the authors' point about the similarity being closer to OE, a baseline which the authors discuss and compare against. Further, even when considered as a variant of OAT for the label noise setting, the present paper contributes to theoretical understanding of such out-of-distribution regularization schemes.

The authors are encouraged to incorporate the reviewers' suggestions, include a discussion of OAT, and contrast their method to other regularization schemes for label noise (Wasserstein Adversarial Regularization (WAR) on label noise, ICLR 2020; Robust early-learning: hindering the memorization of noisy labels, ICLR 2021).

It was discovered late in the review process that this paper makes use of the 80 million tiny images dataset, which has been retracted (https://groups.csail.mit.edu/vision/TinyImages/).  Following the NeurIPS ethical guidelines (https://neurips.cc/public/EthicsGuidelines), this dataset should not be used.  For this reason, the paper is being conditionally accepted.
Decision: Accept (Poster)