Summary and Contributions: This paper studies the memorization phenomenon for learning with noisy labels. They theoretically analyze when and why memorization happens, and proposes a method based on semi-supervised learning to address it. Experiments are performed on multiple benchmarks. ** Final review** The authors address my questions well in their response. After reading other reviewers' comments, I'll keep my original score and lean towards accept.
Strengths: 1. This paper is well motivated. It's nice to see in Figure 1 that memorization is prevented. 2. The theoretical analysis sheds some lights on previous methods which use model's output to modify the target. 3. The paper is mostly well-written and easy to understand.
Weaknesses: 1. The memorization effect is not new to the community. Therefore, the novelty of this paper is not sufficiently demonstrated. The authors need to be clearer what extra insights this paper gives. 2. It would be better if the author could provide some theocratical justification in terms of why co-training and weight averaging can improve results, since they are important for the performance. 3. The empirical performance does not seem to be very strong compared to DivideMix. Some explanations are needed.
Correctness: Yes the claims and method are correct.
Clarity: Yes it is well-written in general.
Relation to Prior Work: Yes it is clearly discussed.
Summary and Contributions: One folklore result in deep learning today is that deep neural networks (DNNs) learn the correct labels first before they memorize the examples with noisy labels. One contribution of this paper is to argue that this phenomenon is not restricted to DNNs, by arguing theoretically that a similar behavior can occur in logistic regression in the over-paramterized regime. In turn, this result inspired the development of a new algorithm for learning with noisy labels. The authors show experimentally that their method is competitive with state-of-the-art methods. ============= Post-rebuttal comments: The score was updated based on the authors' response. I had a major concern regarding the proof but the authors' have clarified it in their rebuttal. However, my statement regarding the size of the spheres remains the same but it is not a major objection.
Strengths: Understanding the behavior of deep neural network is a very important research topic. The authors aim to shed some light into one particular empirical observation, namely that examples with correct labels are learned first, by arguing that it holds for simpler models as well, such as logistic regression. Inspired by this, they propose a novel algorithm for learning with noisy labels. Informally, the papers points out that the aforementioned empirical observation has a simple explanation. During the early stage of learning, correct labels will steer gradient descent towards the correct direction whereas noisy labels tend to have a smaller magnitude (since they tend to cancel each other and they are fewer in number). During the later stage of learning, on the hand, the examples with the correct labels will have a small gradient, since they are classified correctly, so gradient descent will be steered mainly by the noisy examples. This is why correct labels are learned first while noisy labels are memorized last. To fix this issue, the author propose adding a regularization term to the objective function. Given the early epochs of training, one may construct an approximation of the true labels upon knowing that noisy labels are learned last, e.g. using temporal averaging. With this "new" target t, a regularization term is added so that the prediction of the model does not deviate much from t during the later stages of learning (see Eq 9). Of course, in order for this to work, one has to estimate the true probabilities accurately, and the authors show that previous methods, such as mixup and co-teaching, can help significantly.
Weaknesses: I have many reservation against the claims of the paper. I would appreciate it if the authors can clarify some of these issues during their rebuttal. First, the proof of their main theorem about logistic regression has many issues. One key issue is that the authors make assumptions within the proof that are not clearly stated or justified upfront. For example, in Line 440 in the supplementary materials, the proof assumes that theta^Tv<.1. Where did this assumption come from? There is no reason to expect it to hold apriori. It seems to have been added just to close a hole in the proof but that means Proposition 3 was not really "proven". What is worse is that later in the proof in Line 457, the authors make the *opposite* assumption! A second issue with the theorem is that it assumes a highly contrived setting for logistic regression. First, the claim is that it holds for a "sufficiently small sigma." Basically, the authors show that if we have two spheres (one for the positive class and one for the negative class) and both spheres are *sufficiently tiny* (i.e. all instances are almost identical), then correct labels will be learned first while noisy labels will be learned last. I think this is not surprising since all instances are almost identical and correct labels correspond to the majority in a very tiny ball. The authors argue that they can extend their result to the case where Delta>1/2 but I don't think that is possible. If Delta?1.2, then the role of v and -v would be interchanged (since the majority of labels are flipped). Also, the authors assume that the number of examples n is very close to the problem dimension d. In particular, they assume that 0.75n<d<n. Second, the empirical support the authors provide in Figures A1 and B1 does not actually show the main phenomenon that was cited earlier for deep learning. The main interesting phenomenon is that noisy labels tend to be classified correctly early during training before their noisy labels are memorized. So, the accuracy within the noisy set is not a monotone function: it goes up initially before it drops. This is shown in Figure 1:Top-right. Figures A1 and B1 for regression do not show that phenomenon. Memorization in Figure A.1 takes effect almost immediately after very few iterations. Finally, the algorithm does not outperform state-of-the-art methods. Given the concerns above and the fact that there is no significant improvement experimentally, I think the paper is not suitable for publication at NeurIPS.
Correctness: No, I think there are issues with the proofs and the setting. Please see my detailed comments above.
Clarity: Yes, the paper is well-written.
Relation to Prior Work: Yes, relation to previous works is clearly discussed.
Additional Feedback: 1- Some of the figures contain redundant information, which can be confusing. For example, the two curves in Figure 1 left convey the same information (blue = 1 - green). Having one curve would be better. The same holds for Figure A1. Also, in Figure B1, the right column can be inferred from the left column (right column is the absolute value of the left). 2- Putting the issues with the proof of the theorem aside, the method proposed here is actually independent of the argument for logistic regression. It is inspired by the form of the gradient in Eq 5, the heuristic argument in Section 4.1 , and the empirical support in Figure 2. I don't think including the result on logistic regression adds value and the paper would be stronger without it. 3- - The experiments in Table 1 show an improvement of ELR but it is not clear if the improvement is due to the algorithm itself (i.e. the regularization term) or the improvement is due to the new target t, which is estimated independently of ELR. It is possible that ELR is not really needed. There are two typos: - In Line 46, I think the authors meant to say that they do NOT assume that the correct classes are known. - In Line 109, the minimization of Theta is over the space R^(2xp), not R^p.
Summary and Contributions: This paper focuses on coping with label noise by exploiting a regularization term. This term seeks to maximize the inner product between the output of model and the targets, which prevents memorization of noisy labels. The authors provide a detailed theoretical analysis for memorization effect of neural networks. Extensive experiments are conducted to verify the effectiveness of the proposed method. **After reading author response**: I have read the response and comments from other reviewers. The author cleared my doubts well. Thus, I will keep my initial score.
Strengths: 1. The writing logic of this paper is good. 2. Complete and meaningful theoretical proof. 3. Sufficient experimental results which validate the effectiveness of ELR or ELR+, especially in noisy synthetic datasets.
Weaknesses: 1. Some experimental results are not convincing. In the experiments on real-world dataset, the advantage of the proposed method is weak. On Clothing1M, ELR and ELR+ employ some tricks to get better performance. On ILSVRC12, DivideMix performs much better than ELR and ELR+. 2. There are many hyper-parameters needed to be considered. As shown in Figure G.1, the proposed method is sensitive to \lambda. 3. Some details should be added. For example, when ELR or ELR+ achieves worse performance than baselines, the authors should discuss the potential problem of the proposed method or analyze the reason for this phenomenon.
Correctness: Yes. The claims and method are correct. The empirical methodology is correct.
Clarity: Yes, the paper is well written.
Relation to Prior Work: Yes. It is clearly discussed.
Summary and Contributions: This work introduces a novel regularization method to prevent memorization of false labels in classification tasks. The authors show that early learning followed by memorization of false labels occurs in simple linear generative models then argue that deep non-linear models show similar behavior. The proposed regularization method relies on techniques from semi-supervised learning to reduce the gradients of noisy examples by using a running average of the model's outputs. This preserves the correct classifications of noisy labels that the authors claim are present in the early learning phase. The authors propose additional extensions: computing the running average of model outputs from an ensemble of past models using weight averaging, predicting targets from outputs of two separate networks, and the use of mix-up data augmentation. The authors demonstrate the technique is effective and produces competitive results on various synthetic and real datasets. **Upon reviewing author responses**: I still believe the paper should be accepted, so I will keep my score at 7.
Strengths: * The authors present a simple linear model example that illustrates the concept of the early learning phase and helps the reader understand how this can lead to memorization when some fraction of the dataset contains noisy labels. This analysis is a nice stepping stone for the reader to the regularization technique they propose. * The authors motivate the definition of the regularization term with a toy example and plots that illustrate the effect of this regularization and how it balances with the cross entropy term. * The authors show promising results with ResNet-34 on CIFAR-10 and CIFAR-100 with symmetric and asymmetric label noise, setting a new state of the art benchmark on this task amongst techniques that only modify the training loss. * The proposed method is competitive with techniques that modify sample selection and data augmentation on CIFAR-10. The SOTA technique DivideMix that they compare with is significantly more complicated than the proposed regularization technique, requiring at least double the computational complexity of a given model due to the need to train multiple versions of the model. I am unfamiliar with this area so I cannot speak to the novelty of the technique and its relation to prior work. Improving classifiers and principled approaches to handling the real-world problem of label noise is of great interest to ML practitioners in the NeurIPS community. Improving understanding of training dynamics in classification models is of interest to theorists.
Weaknesses: * Implementation requires a parameter (the target vector) that scales with the size of the dataset. This may be impractical for very large datasets.
Correctness: I did not notice any correctness issues with the claims and method; however, I am inexperienced in this area.
Clarity: The paper is easy to understand and progressively grows the reader's understanding with a mix of plain descriptions in the prose and light derivations with pointers to the appendix where relevant. I appreciated the writing style.
Relation to Prior Work: The related work section appears robust, but I am not experienced in this domain.
Additional Feedback: Code samples would aid in reproducibility, but the method seems as though it would be easy to implement from the authors' description.