CLIPCleaner: 利用CLIP清洗噪声标签
CLIPCleaner: Cleaning Noisy Labels with CLIP
AI总结：
针对噪声标签学习中的自我确认偏差和硬噪声问题，提出利用CLIP构建零样本分类器进行离线单步干净样本选择的CLIPCleaner方法，性能更优且是首个用视觉语言模型解决该问题的研究。
摘要：
噪声标签学习（Learning with Noisy labels, LNL）是机器学习领域面临的一项重大挑战。一些被广泛使用的方法会选择训练中模型置信度高的样本作为干净样本，例如“小损失”法，但这类方法会受到所谓“自我确认”偏差的影响。这种偏差的产生是因为训练中的模型至少部分是在噪声标签上训练的。此外，在分类任务中还存在另一项挑战：部分标签噪声出现在视觉上非常相似的类别之间（即“硬噪声”）。本文针对这些挑战提出了CLIPCleaner方法，该方法利用强大的视觉语言（Vision-Language, VL）模型CLIP构建零样本分类器，用于高效的离线干净样本选择。这种方法的优势在于样本选择与训练中的模型解耦，并且由于CLIP的训练方式，样本选择能够感知类别之间的语义和视觉相似性。我们提供了理论依据和实证证据，证明与传统预训练模型相比，CLIP在噪声标签学习任务中更具优势。与结合迭代样本选择和多种技术的现有方法相比，CLIPCleaner提供了一种简单的单步方法，在基准数据集上取得了具有竞争力甚至更优的性能。据我们所知，这是首次将视觉语言模型用于样本选择以解决噪声标签学习问题，凸显了这类模型在该领域的潜力。
英文摘要：
Learning with Noisy labels (LNL) poses a significant challenge for the Machine Learning community. Some of the most widely used approaches that select as clean samples for which the model itself (the in-training model) has high confidence, e.g., `small loss', can suffer from the so called `self-confirmation' bias. This bias arises because the in-training model, is at least partially trained on the noisy labels. Furthermore, in the classification case, an additional challenge arises because some of the label noise is between classes that are visually very similar (`hard noise'). This paper addresses these challenges by proposing a method (\textit{CLIPCleaner}) that leverages CLIP, a powerful Vision-Language (VL) model for constructing a zero-shot classifier for efficient, offline, clean sample selection. This has the advantage that the sample selection is decoupled from the in-training model and that the sample selection is aware of the semantic and visual similarities between the classes due to the way that CLIP is trained. We provide theoretical justifications and empirical evidence to demonstrate the advantages of CLIP for LNL compared to conventional pre-trained models. Compared to current methods that combine iterative sample selection with various techniques, \textit{CLIPCleaner} offers a simple, single-step approach that achieves competitive or superior performance on benchmark datasets. To the best of our knowledge, this is the first time a VL model has been used for sample selection to address the problem of Learning with Noisy Labels (LNL), highlighting their potential in the domain.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
