Title: Noisy Multi-Label Learning through Co-Occurrence-Aware Diffusion
Venue: NeurIPS 2025
Accepted: yes
Keywords: Multi-label classification; Noisy multi-label learning; Diffusion model; Co-occurrence-aware
Abstract: Noisy labels often compel models to overfit, especially in multi-label classification tasks. Existing methods for noisy multi-label learning (NML) primarily follow a discriminative paradigm, which relies on noise transition matrix estimation or small-loss strategies to correct noisy labels. However, they remain substantial optimization difficulties compared to noisy single-label learning. In this paper, we propose a Co-Occurrence-Aware Diffusion (CAD) model, which reformulates NML from a generative perspective. We treat features as conditions and multi-labels as diffusion targets, optimizing the diffusion model for multi-label learning with theoretical guarantees. Benefiting from the diffusion model's strength in capturing multi-object semantics and structured label matrix representation, we can effectively learn the posterior mapping from features to true multi-labels. To mitigate the interference of noisy labels in the forward process, we guide generation using pseudo-clean labels reconstructed from the latent neighborhood space, replacing original point-wise estimates with neighborhood-based proxies. In the reverse process, we further incorporate label co-occurrence constraints to enhance the model's awareness of incorrect generation directions, thereby promoting robust optimization. Extensive experiments on both synthetic (Pascal-VOC, MS-COCO) and real-world (NUS-WIDE) noisy datasets demonstrate that our approach outperforms state-of-the-art methods.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes a diffusion-based method coupled with neighborhood-based co-occurrence to handle the multi-label learning problem. In particular, the proposed method is inspired by the previous method CARD designed on single noisy label learning by firstly estimating the noisy multi-label distribution through the K nearest neighbor approach. Secondly, the obtained distribution of noisy multi-label matrix follows the diffusion process, and then reconstructed in the backward diffusion process as done in DDPM. The empirical evaluation on both synthetic and real-world datasets show the remarkable performance of the proposed method compared to the existing ones.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths:\nThe paper is motivated from the real world setting where noisy labels may be contaminated into the set of multi-labels provided. Hence, it is important to address the noisy multi-label learning problem. The paper also provides a good overview and literature review on the previous and existing studies in the field.\n\nThe explanation of the proposed method is easy to follow, especially the visualization. In addition, the paper also presents extensive empirical evaluation to demonstrate the capability of their proposed method.\n\n## Weaknesses:\n- The proposed method is limited in terms of novelty. As described in the paper, it is an adopted version of CARD in noisy single-label learning to apply to the multi-label learning. Due to this similarity, it reduces the originality of the proposed method.\n- Another concern is that diffusion models, in general, are designed to estimate the distribution of data. In this case, the data is the noisy multi-labels. The nearest neighbor step in the forward pass already provides a rough estimation of that distribution. The backward pass is to provide a \"finer grain\" version of that distribution. However, it is considered as the clean label distribution that is \"denoised\" by the diffusion model.\n\n## Minors:\n- Inconsistent English: the majority of the paper follows the American English, but some are in British English. For example, line 24: \"expert-labelled\" ==> \"expert-labeled\"\n- Eqs (4) and (5) have unnecessary line break for a single column paper.\n\nQUESTIONS:\nPlease address the two questions raised in the weaknesses. In particular, please explicitly provide the distinctions between CARD and the proposed method.\n\nLIMITATIONS:\nThe paper briefly discusses the limitations of the proposed method, which are more in the empirical evaluation, such as instance-dependent noise or out-of-distribution noise. There is no explicit discussion on the limitations of the proposed method.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-27"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a Co-Occurrence-Aware Diffusion (CAD) model, which reformulates noisy multi-label learning (NML) from a generative perspective.  It treats features as conditions and multi-labels as diffusion targets, optimizing the diffusion model for multi-label learning with theoretical guarantees.  To mitigate the interference of noisy labels in the forward process, it guides generation using pseudo-clean labels reconstructed from the latent neighborhood space, replacing original point-wise estimates with neighborhood-based proxies.  In the reverse process, it incorporates label co-occurrence constraints to enhance the model’s awareness of incorrect generation directions, thereby promoting robust optimization.  Experiments demonstrate the effectiveness of the proposed method.  The main contributions of this paper are:\n\n- It reframes NML as a robust label generation task based on diffusion models with theoretical deduction, and enhances the mapping between features and multi-labels through matrix-based label representation.\n- It designs a pseudo-clean label reconstructor and a meta-label co-occurrence matrix estimator, leveraging pre-trained encoders to provide strong priors for diffusion model training.\n- It integrates co-occurrence constraints into the diffusion modeling, proposing the Co-Occurrence-Aware Diffusion (CAD) model, which can robustly learn the generative mapping from features to true multi-labels.\n\nSTRENGTHS AND WEAKNESSES:\n**Strength**\n\n- The problem studied in this paper is interesting and valuable.\n- This paper is well written and in good sharp, which is easy to follow.\n- Experimental results are promising and can validate the effectiveness of the proposed method.\n\n\n\n**Weakness**\n\n- For the motivation of this paper, in my opinion, it does not clearly explain why a diffusion model is preferred over other generative frameworks on the NML problem.  Therefore, the practical requirements for CAD are somewhat unclear. The manuscript lacks a detailed discussion of the practical requirements and benefits that CAD fulfills over alternative frameworks. As a result, readers are left without a clear understanding of why the diffusion paradigm was chosen, what specific challenges in modeling noisy label matrices it addresses, and how its properties translate into tangible performance or stability advantages in the multi‑label denoising context.\n- The proposed CAD seems to rely heavily on a strong pre‑trained encoder.  However, the paper does not explore scenarios in which such powerful pre‑trained models are unavailable, such as domains with limited data or modalities without large-scale pre-training assets. There is no evaluation of CAD‘s performance when the encoder is trained from scratch, initialized with random weights, or borrowed from a weaker backbone architecture.  Without such analysis, it remains unclear whether the benefits of CAD stem primarily from the diffusion framework itself or are largely inherited from the strength of the underlying feature representations.\n\nQUESTIONS:\n1.  Why is a diffusion model preferred over other generative frameworks on the NML problem?\n2.  How does CAD perform when trained from scratch or with a weakly initialized encoder?\n\nLIMITATIONS:\nThe authors could further discuss the potential societal impact of the proposed method.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes to learn the posterior mapping from features to labels in the noisy multi-label problem using the diffusion model. The noisy issue is constrained by using pseudo-clean labels in the generation process. The pseudo-clean labels are obtained from the latent neighborhood space, replacing the point-wise estimates in traditional methods. The proposed method is evaluated on both synthetic and real-world noisy datasets.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. The paper is well written and most information is clearly presented.\n\n2. The idea of using the diffusion model to address the noisy multi-label learning problems is novel and interesting. \n\n3. The experiments show that the proposed method has an advantage compared to existing noisy multi-label learning methods.\n\nWeaknesses:\n\n1. The proposed method seems dependent on the label correlation being associated with the latent space similarity. However, in some tasks and domains, it might not be applicable. In these cases, the diffusion model generation might not meet the requirement for pseudo-clean labels. There might also be issues when there are both noisy labels and out-of-distribution data samples. These are not well discussed in the paper.\n\n2. The paper mentions theoretical guarantees, but it is unclear what the guarantees are referring to.\n\n3. The paper does not discuss other possible ways of mitigating the noise issue using the latent neighborhood space, such as other automatic correction models.\n\n4. The paper does not discuss the possibility of damaging the rare labels, which may occur simultaneously with other unexpected labels.\n\nQUESTIONS:\n1. What happens if the label correlation is less associated with the latent space similarity? What happens when the domain knowledge is very different from pre-trained knowledge?\n\n2. What are the exact theoretical guarantees?\n\n3. What is the performance on rare labels?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper reframes NML as a robust label generation task based on diffusion models with theoretical deduction, and enhances the mapping between features and multi-labels through matrix-based label representation. The paper designs a pseudo-clean label reconstructor and a meta-label co-occurrence matrix estimator, leveraging pre-trained encoders to provide strong priors for diffusion model training. Besides, the paper integrates co-occurrence constraints into the diffusion modeling, proposing the Co-Occurrence-Aware Diffusion (CAD) model, which can robustly learn the generative mapping from features to true multi-labels.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\n(1) The paper is well motivated and is related to an important task in practice.\n\n(2) The paper formulation is novel and the developed approach has good intuitions.\n\n(3) The experiments are comprehensive and the developed approach achieves strong performances. \n\nWeaknesses\n\n(1) It would be great if the paper can provide some analysis related to the computational costs of the developed approach and the baselines.\n\n(2) It would be great to provide more case study results to illustrate how the developed key components work in practice. Some intermediate results may also be presented.\n\nQUESTIONS:\n(1) It would be great if the paper can provide some analysis related to the computational costs of the developed approach and the baselines.\n\n(2) It would be great to provide more case study results to illustrate how the developed key components work in practice. Some intermediate results may also be presented.\n\n(3) Some discussed previous works (e.g., [20]) provide theoretical results. Might it be feasible to provide some theoretical results similarly in this paper? Some discussions would be very insightful.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-04"}