Kati8/ICLR_2023_2025 huggingface dataset:
authors: Jindong Wang
authors: Ankit Shah
authors: Ran Tao
authors: Hongxin Wei
authors: Xing Xie
authors: Masashi Sugiyama
authors: Bhiksha Raj
reviews: 
Comments: This paper presents a novel topic of studying the effect of pre-training noise on various downstream datasets, termed noisy model learning.
The authors conduct the empirical study and analysis of noisy ImageNet and YFCC15M of supervised pre-trained and contrastive pre-trained ResNet50 models and illustrate that slight noise in pre-training improves performance on in-domain downstream tasks but always hurts the performance on out-of-domain tasks. From the singular value analysis of the pre-trained feature space, the authors designed two metrics that in general align with the downstream empirical observations.
The authors also propose several regularization terms based on the singular values of features that can mitigate the noise in pre-training in a block-box tuning manner. The authors provide comprehensive experiments to verify the effectiveness of the proposed method and offer interesting analyses and discussions.
Strengths: The paper is generally well-written and organized.
The authors provide a first novel and interesting study on the effect of pre-training noise, demonstrating the importance of this research topic, especially in the context of large foundation models. 
The empirical study for revealing the effect of pre-training noise is extensive and comprehensive, including both in-domain and out-of-domain datasets from various distributions. 
The proposed method may not be very innovative, but it is simple and verified on both CV and NLP tasks with different large backbones. The method also works in the API case mentioned in the paper.
The authors also additionally study the combination of the proposed noisy model learning and traditional noise label learning, demonstrating the effect of noise in pre-training also exists when downstream data has noise. 
The detailed results, experiments setup, and ablation study are presented in the Appendix.
Weaknesses: How to introduce synthetic noise in ImageNet and YFCC15M needs more explanation.
The pattern SVE analysis of the ImageNet model and YFCC15M model are slightly different in Fig.3, and perhaps need more explanation.
Recommendation: 10: strong accept, should be highlighted at the conference
Reviewer confidence: 5: You are absolutely certain about your assessment. You are very familiar with the related work and checked the math/other details carefully.
Soundness/correctness: 3 good
Clarity: 4 excellent
reviews: 
Comments: This work aims to study the noise in pertaining data and its impact on downstream tasks. The authors exploit the Singular Value Entropy (SVE) and the Largest Singular Value Ratio (LSVR) to analyze the singular value spectrum of the pre-trained feature space, and discover that proper noise in pre-training data increases both SVE and LSVR, leading to better transferability and worse robustness. Based on the observations, the authors further introduce an MLP together with three regularizations to transform the pre-trained features into a better feature space. Experiments with different model architectures and datasets are conducted to demonstrate the effectiveness of the proposed method.
Strengths: - The analysis of feature space with the singular value spectrum is interesting and meaningful. Rich experiments and analyses are conducted to show how the noise in pertaining data can impact the learned feature embedding.
- The proposed regularizations are intuitive and effective. Extensive comparisons are presented to show the improvements.
Weaknesses: - This paper is featured with extensive empirical results. However, the core techniques in methodology (analysis and regularization of singular value spectrum ) have been studied in existing works[e.g. Chen et al., 2019, Bardes et al. 2022], which may undermine the theoretical contribution of this work.

- Some figures are hard to understand by themselves. E.g. different types of marks are cluttered in Fig3.
Recommendation: 8: accept, good paper
Reviewer confidence: 3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.
Soundness/correctness: 3 good
Clarity: 3 good
reviews: 
Comments: The paper addresses the challenges posed by label noise in pre-training datasets and its impact on downstream tasks. The authors focus on supervised pre-training models using synthetic noisy ImageNet-1K and YFCC15M datasets. They observe that while slight noise in pre-training can enhance in-domain (ID) transfer performance, it consistently harms out-of-domain (OOD) performance. The reason behind is noise in pre-training shapes the feature space differently. They introduce a lightweight black-box tuning method, NMTune, to mitigate the adverse effects of noise and improve generalization on both ID and OOD tasks.
Strengths: - This paper studies a problem that is both practical and significant, yet has not been sufficiently investigated in prior research.
- This paper is well-motivated and easy to follow.
- The analysis of features is useful to understand the noise's impact on ID and OOD data. 
- Experiments are comprehensive.
Weaknesses: - Improvements are needed in the writing and presentation quality. Please check the Question section below. 
- Some claims in the paper are unclear and confusing. Please check the Question section below. 
- The authors did not mention the limitations of their method and potential future work. The paper does not explore or discuss potential failure cases of the proposed methods. Understanding when and why the methods might fail is crucial for practical applications
Recommendation: 8: accept, good paper
Reviewer confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Soundness/correctness: 3 good
Clarity: 3 good
reviews: 
Comments: This work introduces an important investigation into how pre-training noise impacts performance across different downstream applications, referred to as noisy model learning. The authors execute a thorough empirical investigation examining noisy versions of ImageNet and YFCC15M, comparing both self-supervised and supervised pre-trained ResNet50 variants. Their findings demonstrate that moderate noise during pre-training can enhance results on domain-specific downstream tasks but consistently degrades performance on out-of-domain applications. Leveraging singular value decomposition analysis of learned representations, the authors develop two quantitative metrics that generally correspond with the observed downstream performance patterns. Additionally, the authors introduce several regularization approaches grounded in feature singular values that can reduce the impact of pre-training noise through parameter-efficient fine-tuning. The work is supported by extensive experimental validation and offers valuable insights and interpretations.
Strengths: The manuscript demonstrates clear writing and logical structure throughout. This represents a novel and timely examination of pre-training noise effects, highlighting the significance of this research direction particularly given the current landscape of large-scale foundation models. The empirical analysis is thorough and multifaceted, encompassing both domain-aligned and domain-misaligned evaluation scenarios with diverse data distributions. While the proposed technique may not represent a substantial methodological contribution, it is practical and straightforward, with validation across both computer vision and natural language processing domains using various large-scale architectures. The approach also demonstrates applicability in black-box API scenarios as discussed. Furthermore, the authors conduct additional analysis examining the interaction between pre-training noise and downstream label noise, showing that noise effects persist in this combined setting. Comprehensive experimental details, implementation specifics, and detailed ablation analyses are included in the supplementary material.
Weaknesses: The methodology for generating synthetic noise in ImageNet and YFCC15M requires additional clarity and justification. The singular value evolution patterns shown in Fig.3 exhibit notable differences between the ImageNet and YFCC15M models, which warrant more thorough explanation and discussion.
Recommendation: 10: strong accept, should be highlighted at the conference
Reviewer confidence: 5: You are absolutely certain about your assessment. You are very familiar with the related work and checked the math/other details carefully.
Soundness/correctness: 3 good
Clarity: 4 excellent