论文标题
基于对比度学习的特征提取框架和自适应阳性和负样本
Feature Extraction Framework based on Contrastive Learning with Adaptive Positive and Negative Samples
论文作者
论文摘要
在这项研究中，我们提出了一个基于对比度学习的特征提取框架，并具有适应性正面和负面样品（CL-FEFA），适用于无监督，监督和半监督的单视图提取。 CL-FEFA从特征提取的结果中自适应地构造了正和负样本，这使其更合适和准确。此后，根据以前的正面和负样本根据Infonce损失而重新提取判别特征，这将使阶级样品更加紧凑，并且类间样本更加分散。同时，使用子空间样本的潜在结构信息动态构建正面和负样本可以使我们的框架对嘈杂数据更加可靠。此外，CL-FEFA考虑了阳性样品之间的相互信息，即潜在结构中的相似样品，这为其在特征提取方面的优势提供了理论支持。最终的数值实验证明，所提出的框架比传统的特征提取方法和对比度学习方法具有强大的优势。
In this study, we propose a feature extraction framework based on contrastive learning with adaptive positive and negative samples (CL-FEFA) that is suitable for unsupervised, supervised, and semi-supervised single-view feature extraction. CL-FEFA constructs adaptively the positive and negative samples from the results of feature extraction, which makes it more appropriate and accurate. Thereafter, the discriminative features are re extracted to according to InfoNCE loss based on previous positive and negative samples, which will make the intra-class samples more compact and the inter-class samples more dispersed. At the same time, using the potential structure information of subspace samples to dynamically construct positive and negative samples can make our framework more robust to noisy data. Furthermore, CL-FEFA considers the mutual information between positive samples, that is, similar samples in potential structures, which provides theoretical support for its advantages in feature extraction. The final numerical experiments prove that the proposed framework has a strong advantage over the traditional feature extraction methods and contrastive learning methods.
