---
# System prepended metadata

title: NeurIPS23 Rebuttal

---

## General Response

We deeply appreciate the reviewers' positive feedback and valuable comments. Most reviewers agreed that (1) the problem setting and methodology are reasonable and novel, (2) the theoretical analysis of the methodology is sound, and (3) the evaluation was performed extensively. Because the reviewer's comments are mostly about the experiments, we have significantly improved (and are improving) the evaluation section during the rebuttal period by adding two baselines, one dataset, and one metric. (See the attached PDF file for an enhanced plot.) Therefore, we believe that the superiority of Prune4Rel over other baselines has demonstrated much more clearly and hope that the remaining concerns are addressed by the rebuttal. We are happy to answer additional questions during the discussion period.


## R1 (Score 7, Conf 4)

We sincerely appreciate the reviewers' constructive comments and positive feedback on our manuscript.

`Q1. Uniform sampling is usually the second-best baseline. It performs especially well in CIFAR-10N Random/Worst and Clothing-1M. Can the authors elaborate more on this?`

To improve the performance of Re-labeling models, sample selection should achieve a balance between easy (clean) and hard (possibly noisy) examples. This is because the easy (clean) examples support the correct relabeling of neighbor hard examples, while the hard examples greatly boost the test accuracy if they are re-labeled correctly. Existing data pruning algorithms that prefer hard examples may perform poorly due to a lack of easy examples that support accurate relabeling, and clean sample selection algorithms that prefer easy examples may also perform poorly due to a lack of hard (informative) examples that aid in test accuracy after relabeling. On the other hand, uniform sampling shows somewhat robust performance by selecting easy and hard examples in a balanced way.

For CIFAR-10N, as shown in the selection ratio of 0.2 in Table 1, uniform sampling was more effective at a small selection ratio because a proper amount of easy (clean) examples was collected to support accurate relabeling. However, looking at the selection ratio of 0.8, uniform sampling tended to show lower performance than other data pruning baselines since selecting hard examples became more important as there existed already proper amounts of easy examples. For Clothing-1M, we conjecture that uniform sampling performed well because a low selection ratio was used for testing the fine-tuning. Overall, we will add this discussion in the final version.


`Q2. The derivation from the reduced neighborhood confidence (Eq. 3) to the empirical reduced neighborhood confidence (Eq.4) is confusing. What motivates using the cosine distance to perform a weighted sum? Is this motivated by importance sampling?`

The reduced neighborhood confidence (Eq. 3) of each example is calculated as the sum of the confidences in its neighborhood. However, identifying the neighborhood of an example is very expensive and thus practically infeasible. Thus, instead of explicitly finding the neighborhood of each example, the *empirical* reduced neighborhood confidence (Eq. 4) approximates it using the cosine similarity **as the likelihood of belonging to the neighborhood**. We will elaborate on this rationale in the final version.


`Q3. WebVision and Clothing-1M are crawled from the web, but Prune4ReL seems to work better in Clothing-1M. Can the authors elaborate on this further?`

We interpret your question to mean that Prune4Rel achieves higher accuracy at lower selection ratios in Clothing-1M. Following the existing literature[a] for Clothing-1M, we used a ResNet model pretrained on ImageNet, as specified in Section 4.1. Thus, **fine-tuning was tested for Clothing-1M**, and this setting explains why high accuracy was achieved using a small portion of the Clothing-1M training set. Please inform us if we misunderstood your question.

[a] Sheng Liu, Zhihui Zhu, Qing Qu, Chong You: Robust Training under Label Noise by Over-parameterization. ICML 2022: 14153-14172


`Q4. kCenter and GraNd are missing in Figure 3b. The authors have to find a better way to visualize this plot (Figure 3b). Try to zoom in a little bit.`

Thanks for pointing out this issue. The enhanced plot is included in the pdf file for the global response. Since *kCenter* requires huge computation and memory costs, it is not feasible to run in our environment (refer to Figure 3c). Also, since *Forgetting* requires prediction history during the warm-up training period to calculate the forgetting score of each training example, we could not run it for Clothing-1M where sample selection is performed from the pre-trained model.


`Q5. Section 4.5 is like the motivation of the paper. Consider re-ordering them.`

Thank you very much for helping us improve our paper. As you propely recognize, utilizing Re-labeling models for data pruning under label noise is much more effective than utilizing standard models. Thus, we will reorder Section 4.5 to Section 4.3 in order to confirm the motivation early on. In addition, we will add a summary of Section 4.5 at the end of Introduction: "When combined with the pruning methods, the performance of the Re-labeling models significantly surpasses that of the standard models in CIFAR-10N and CIFAR-100N by up to 21.6%."


`Q6. Some notations are not clear.`
> In Definition 3.1, the dimensionality of $x$ is set to be 512 for CIFAR-10N and CIFAR-100N and 2048 for WebVision and Clothing-1M. We will include this detail in the final version.

> In Theorem 3.4, we will correct the notation to $\mathcal{S} \subseteq \tilde{\mathcal{D}}$. Thank you for pointing out the typo.




## R2 (Score 4, Conf 5)

`The writing of this article is very clear and easy to follow up. Moreover, the methodology in this paper is also reasonable with necessary theoretical analysis. I enjoy this work very much!`
> We are very glad to hear that you enjoy reading our paper.

`My only concern about this work is the experimental part.`
> We did our best to improve the evaluation during the rebuttal period, and we hope that our efforts will address your sole concern.


`Q1. Recent works like [a,b,c] are missing in those selected baselines.`

Thank you very much for recommending important references. We have additionally conducted the experiments with Moderate[a] and SSP[b], and the results with SOP+ are reported as below. **Prune4Rel is also shown to outperform these recent baselines**, because they are not designed for the **noise-robust** learning scenario. We will add these results (also for other datasets) in the final version.

| Methods        |         | CIFAR-10N | Random   |          |         | CIFAR-10N | Worst    |          |
| -------------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- |
|                | 0.2      | 0.4      | 0.6      | 0.8      | 0.2      | 0.4      | 0.6      | 0.8      |
| Uniform  | 87.5     | 91.5     | 93.4     | 94.8     | 81.9     | 87.5     | 90.8     | 91.8     |
| SmallL   | 77.6     | 86.2     | 90.7     | 94.3     | 78.8     | 84.1     | 89.3     | 92.3     |
| Margin   | 52.1     | 79.6     | 92.6     | 95.1     | 45.7     | 61.8     | 84.6     | **92.5** |
| kCenter  | 86.3     | 92.2     | 94.1     | **95.3** | 81.9     | 88.0     | 91.3     | 92.3     |
| Forget   | 82.4     | 93.0     | 94.2     | 95.0     | 71.1     | 87.7     | 90.6     | 92.2     |
| GraNd    | 24.2     | 51.6     | 85.9     | 94.9     | 15.4     | 25.7     | 51.0     | 86.8     |
| **Moderate** | 88.3     | 92.8     | 94.1     | 94.6     | 75.0     | 81.9     | 87.7     | 91.8     |
| **SSP** | 80.5     | 91.7     | 93.8     | 95.0     | 70.8     | 86.6     | 89.2     | 92.3     |
| **Pr4Rel**     | 88.2     | 93.0     | **94.4** | 95.1     | 83.4     | **89.3** | **91.5** | **92.5** |
| **Pr4Rel$_B$** | **88.6** | **93.1** | 94.2     | **95.3** | **84.2** | 89.1     | 91.3     | **92.5** |

We were unable to add CCS[c] within a week, but the results for CCS will be included in the 
version.

[a] Xiaobo Xia, Jiale Liu, Jun Yu, Xu Shen, Bo Han, Tongliang Liu: Moderate Coreset: A Universal Method of Data Selection for Real-world Data-efficient Deep Learning. ICLR 2023
[b] Ben Sorscher, Robert Geirhos, Shashank Shekhar, Surya Ganguli, Ari Morcos: Beyond neural scaling laws: beating power law scaling via data pruning. NeurIPS 2022
[c] Haizhong Zheng, Rui Liu, Fan Lai, Atul Prakash: Coverage-centric Coreset Selection for High Pruning Rates. ICLR 2023


`Q2. Lack of results on ImageNet-1K, which is the most convincing part for us.`

Thank you very much for helping us improve our paper. All datasets used for our experiments, CIFAR-10N, CIFAR-100N, WebVision, and Clothing-1M, have **real** label noises. However, for ImageNet-1K, we could not find its variant that contains real label noises. Thus, we have injected synthetic noises into ImageNet-1K. In detail, we flipped the original label (e.g., $classId = 0$) to an incorrect label (e.g., $classId = 1$) randomly by a given noise rate. The results for ImageNet-1K are provided as below and will be included in the final version. Here, the noise rate is set to be 20%, and SOP+ is trained for 50 epochs from scratch with a batch size of 64. Prune4Rel is shown to perform the best also in this dataset, thereby demonstrating its versatility. (More results for high selection ratios will come during the discussion phase.)

| Methods         | ImageNet-1K | 20% Noise |     |
| --------------- | ------- | -------- | -------- |
| Selection Ratio | 0.01    | 0.05     | 0.1      |
| Uniform         | 2.6     | 27.8     | 42.5     |
| SmallL          | 5.8     | 22.8     | 31.4     |
| Forget          | 0.8     | 4.1      | 8.3      |
| **Pr4Rel$_B$**  | **6.0** | **30.2** | **44.3** |


`Q3. I think the real application scenario of dataset-pruning should be the most popular tasks such as large language model (LLM) training and multi-modal training. So, what do you think is the main difficulty of using dataset-pruning for these tasks? How is these tasks different from image classification?`

This is an excellent question. We agree with you that data pruning for multi-modal training and large language model (LLM) training will be more interesting than for image classification. Regarding LLM training, we believe that there are several challenges, compared to image classification. First, various levels---token, sentence, and document---can be considered as a pruning granularity for a large-scale corpus. Second, efficient selection criteria should be developed for LLMs, because most of the metrics typically used for image classification, e.g., gradients, are very expensive to calculate. Obviously, there will be additional challenges to investigate. After the review period concludes, we, the authors and the reviewer, can even attempt to collaborate on this extremely intriguing topic.


`Q4. Can we say that manual filtering is still far better than algorithmic automated filtering? Or, what in the world is causing such a big gap?`

Again, thank you for posing a very interesting research question. In our opinion, the quality, diversity, and redundancy of a given raw dataset are more important in determining the efficacy of data pruning than whether it is performed automatically or manually. (The capacity of a pre-trained model is also relevant given that [d] involves data pruning for fine-tuning.) Clearly, additional research is required to provide an exact answer to this question.

[d] Chunting Zhou et al.: LIMA: Less Is More for Alignment. CoRR abs/2305.11206 (2023)


-----
We are happy to hear that you are very satisfied with our response. Also, even though the experiment for ImageNet-1K is not yet complete, we would like to update the progress using the interim results at this time. Prune4Rel is shown to maintain its dominance also in this dataset.

| Methods         | ImageNet-1K | 20% Noise |     |  |  |
| --------------- | ------- | -------- | -------- | -------- | -------- |
| Selection Ratio | 0.01    | 0.05     | 0.1      | 0.2      | 0.4      |
| Uniform         | 2.6     | 27.8     | 42.5     | 52.7     | 59.2     |
| SmallL          | 5.8     | 22.8     | 31.4     | 42.7     | 54.4     |
| Forget          | 0.8     | 4.1      | 8.3      | 50.6     | 57.2     |
| **Pr4Rel$_B$**  | **6.0** | **30.2** | **44.3** | **53.5** | **60.0** |
    
    
Please let us know if you have any additional questions. Again, your encouragement is greatly appreciated.






## R3 (Score 4, Conf 4)

`Q1. Their empirical results show mixed results in the comparison against existing methods. In Table 1, the results are slightly better than existing baselines on a small number of tasks but predominantly match the performance of existing methods`

Thank you very much for your careful review. We acknowledge your concerns but respectfully argue that **our empirical results are promising** in three ways.

> **(1) Prune4Rel *consistently* outperforms in various datasets and selection ratios.** No baseline consistently shows comparable accuracy in Table 1. To illustrate our claim, the average over different selection ratios for each dataset with SOP+ is calculated as follows. 
 
| Methods        | CIFAR-10N Random | CIFAR-10N Worst | CIFAR-100N Noisy |
| -------------- | -------- | -------- | -------- |
| Uniform        | 91.8     | 88.0     | 56.9     |
| SmallL         | 87.2     | 86.1     | 59.6     |
| Margin         | 79.9     | 71.2     | 42.0     |
| kCenter        | 92.0     | 88.4     | 56.9     |
| Forget         | 91.2     | 85.4     | 55.6     |
| GraNd          | 64.2     | 44.7     | 32.7     |
| **Pr4Rel**     | 92.7     | 89.2     | 59.5     |
| **Pr4Rel$_B$** | **92.8** | **89.3** | **60.8** |

> **(2) The improvement becomes more pronounced when the selection ratio is *relatively low* (e.g., 20% in Table 1).** It is relatively hard to show the difference at high selection ratios, because not many examples are pruned. Table 1 shows that slightly better or comparable accuracy tends to appear at high selection ratios.

> **(3) The results on another dataset reaffirm the superiority of Prune4Rel.** The table in *2 of the reviewer jBWT* is obtained for ImageNet-1K with synthetic flip noises. We train SOP+ for 50 epochs from scratch with a batch size of 64. (More results for high selection ratios will come during the discussion phase.)

Overall, we hope that the reviewer's concerns on empirical results are resolved by our three supporting arguments. We will enrich Table 1 by adding the averages and new results in the final version.


`Q2. In Table 2, it seems that the existing method Forget and kCenter seem to select similar or fewer noisy examples than the proposed method (except in the case of CIFAR-100N with 0.2 or 0.4).`

The goodness of a selected subset cannot be determined based solely on the proportion of noisy examples; the **quality** (or **self-correctability**) of these noisy examples must also be considered. Noisy examples selected by Prune4Rel are mostly self-correctable because it maximizes the total neighborhood confidence of the training set. In contrast, those selected by existing data pruning methods such as kCenter and Forget are ***not*** guaranteed to be self-correctable. To contrast the proportion of self-correctable examples in kCenter, Forget, and Prune4Rel, we plan to expand Table 2 as follows. Here is the proportion of self-correctable examples among the noisy ones within a selected subset at a selection ratio of 0.2.

| Model | Methods    | CIFAR-10N Random |  |  |
| ----- | ---------- | -------- | -------- | -------- |
|       |            | Test Accuracy | % Noisy | % (Self-Correct / Noisy) |
| SOP+  | kCenter    | 86.3     | 19.0 | 75.2     |
|       | Forget     | 82.4     | 17.0 | 61.7     |
|       | **Pr4Rel** | 88.2     | 17.0 | **90.3** |

Therefore, the noisy examples selected by kCenter and Forget may harm the generalizability of a model, whereas those selected by Prune4Rel are rather useful for training. 

In Section 4.3, we meant to say that Prune4Rel selects more high-quality noisy examples aggressively as the subset size increases, based on the increased confidence. We recognize that the current writing is not very clear, and we will revise it accordingly.


`Q3. The authors show in Figure 3 that the test accuracy with SOP+ increases when using a subset produced by the proposed method. However, it is unclear why this is necessarily the case, since there are similar or fewer noisy examples selected by the Forget baseline method.`

This question is answered by the **response to Q2**. The contribution of noisy examples to a model's relabeling capability is low in kCenter and Forget, whereas it is very high in Prune4Rel, despite the fact that the proportion of noisy examples is comparable.


`Q4. The bolding strategies in Table 1 are a bit misleading; in many scenarios, a baseline achieves the same performance as the proposed method (and sometimes achieves a smaller variance), but the proposed method is still listed in bold.`

We apologize for any confusion that our current presentation may have caused. We intended to highlight only our methods, unless they were inferior to any baseline. We will revise the strategy for bolding such that the highest values (including ties) are highlighted. For example, part of Table 1 for SOP+ will be changed to the following.

| Methods        |         | CIFAR-10N | Random   |          |         | CIFAR-10N | Worst    |          |        | CIFAR-100N | Noisy    |          |
| -------------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- |
|                | 0.2      | 0.4      | 0.6      | 0.8      | 0.2      | 0.4      | 0.6      | 0.8      | 0.2      | 0.4      | 0.6      | 0.8      |
| Uniform        | 87.5     | 91.5     | 93.4     | 94.8     | 81.9     | 87.5     | 90.8     | 91.8     | 46.5     | 55.7     | 60.8     | 64.4     |
| SmallL         | 77.6     | 86.2     | 90.7     | 94.3     | 78.8     | 84.1     | 89.3     | 92.3     | 48.5     | 59.8     | 63.9     | **66.1** |
| Margin         | 52.1     | 79.6     | 92.6     | 95.1     | 45.7     | 61.8     | 84.6     | **92.5** | 20.0     | 34.4     | 50.4     | 63.3     |
| kCenter        | 86.3     | 92.2     | 94.1     | **95.3** | 81.9     | 88.0     | 91.3     | 92.3     | 44.8     | 55.9     | 61.6     | 65.2     |
| Forget         | 82.4     | 93.0     | 94.2     | 95.0     | 71.1     | 87.7     | 90.6     | 92.2     | 38.0     | 55.3     | 63.2     | 65.8     |
| GraNd          | 24.2     | 51.6     | 85.9     | 94.9     | 15.4     | 25.7     | 51.0     | 86.8     | 11.0     | 19.0     | 38.7     | 62.1     |
| **Pr4Rel**     | 88.2     | 93.0     | **94.4** | 95.1     | 83.4     | **89.3** | **91.5** | **92.5** | 49.0     | 59.1 | **64.1** | 65.7 |
| **Pr4Rel$_B$** | **88.6** | **93.1** | 94.2     | **95.3** | **84.2** | 89.1     | 91.3     | **92.5** | **52.9** | **60.1** | **64.1** | **66.1** |




## R4 (Score 6, Conf 4)

`Q1. The proposed data pruning method does require model training as many sample selection methods for robust learning do; I think these methods should also be considered as baselines and compared, even though they are not specifically designed for data pruning.` 

Thank you very much for helping us improve our paper. Many sample selection methods, including the small-loss trick, have been developed for the purpose of removing noisy examples from the training set. However, these methods do not consider the importance of examples in terms of re-labeling. Thus, a new data pruning method should ***not*** completely remove noisy examples but select them if they are expected to be relabeled correctly. We expect that other sample selection methods would also perform worse than ours because they do not try to select noisy examples which could be potentially helpful. According to your suggestion, we will try to add more decent sample selection baselines such as Co-teaching+[a] in the final version.

[a] Xingrui Yu, Bo Han, Jiangchao Yao, Gang Niu, Ivor W. Tsang, Masashi Sugiyama: How does Disagreement Help Generalization against Label Corruption? ICML 2019: 7164-7173


`Q2. There are some related work in robust learning field that also leverage neighborhood information, but the authors did not include and discuss them, e.g., "Learning with Neighbor Consistency for Noisy Labels"`

Thank you for bringing up an important topic for discussion. The method NCR[b] that you mentioned utilizes consistency regularization among neighbors so that examples with similar feature representations produce similar outputs. Consequently, NCR and Prune4Rel share the phiolosophy that neighbor examples are useful in the presence of label noise. Nevertheless, the objectives of the two methods to utilizing the neighborhood are distinct: NCR aims to reduce the impact of incorrect labels, whereas Prune4Rel seeks to determine the contribution of an example to the re-labeling accuracy. The final version will include this discussion.

[b] Ahmet Iscen, Jack Valmadre, Anurag Arnab, Cordelia Schmid: Learning with Neighbor Consistency for Noisy Labels. CVPR 2022: 4662-4671