论文标题
在学习与嘈杂标签学习的对比表示方面
On Learning Contrastive Representations for Learning with Noisy Labels
论文作者
论文摘要
深层神经网络能够通过软磁横层（CE）损失轻松地记住嘈杂的标签。先前的研究试图解决此问题的重点是将噪声损失函数纳入CE损失。但是，记忆问题得到了缓解，但仍然由于不可舒适的CE损失所致。为了解决这个问题，我们专注于学习可靠的对比度表示数据，分类器很难记住CE损失下的标签噪声。我们提出了一种新颖的对比正则化函数，以通过标签噪声不主导表示表示的嘈杂数据来学习此类表示。通过理论上研究由提议的正则化功能引起的表示形式，我们揭示了学识渊博的表示形式将信息保留与真实标签和丢弃与损坏标签相关的信息有关的信息。此外，我们的理论结果还表明，学到的表示形式对标签噪声是可靠的。通过基准数据集的实验证明了该方法的有效性。
Deep neural networks are able to memorize noisy labels easily with a softmax cross-entropy (CE) loss. Previous studies attempted to address this issue focus on incorporating a noise-robust loss function to the CE loss. However, the memorization issue is alleviated but still remains due to the non-robust CE loss. To address this issue, we focus on learning robust contrastive representations of data on which the classifier is hard to memorize the label noise under the CE loss. We propose a novel contrastive regularization function to learn such representations over noisy data where label noise does not dominate the representation learning. By theoretically investigating the representations induced by the proposed regularization function, we reveal that the learned representations keep information related to true labels and discard information related to corrupted labels. Moreover, our theoretical results also indicate that the learned representations are robust to the label noise. The effectiveness of this method is demonstrated with experiments on benchmark datasets.
