将对比学习与聚类结合，提升无监督视觉表征质量
Cluster Contrast for Unsupervised Visual Representation Learning
- 用查询-键网络结构，同时实现特征分离与聚合
- 在CIFAR-10上达91.40%线性分类准确率
- 适合追求高性能无监督学习的科研与工程人员
我们提出一种名为聚类对比（Cluster Contrast, CueCo）的新方法，用于无监督视觉表征学习。该方法融合对比学习与聚类的优势，通过两个神经网络——查询网络和键网络（键网络通过查询输出的慢速移动平均更新）——在特征空间中同时实现特征的分散与对齐。利用对比损失使不同类别特征相互远离，增强类间区分度；通过聚类目标使同一簇内特征相互聚集，提升类内紧凑性。在使用ResNet-18主干网络进行线性评估时，该方法在CIFAR-10上达到91.40%的顶1分类准确率，在CIFAR-100上为68.56%，在ImageNet-100上为78.65%。结果表明，结合对比学习与聚类可有效推进无监督视觉表征学习的发展。
原文摘要 · Abstract (English)
We introduce Cluster Contrast (CueCo), a novel approach to unsupervised visual representation learning that effectively combines the strengths of contrastive learning and clustering methods. Inspired by recent advancements, CueCo is designed to simultaneously scatter and align feature representations within the feature space. This method utilizes two neural networks, a query and a key, where the key network is updated through a slow-moving average of the query outputs. CueCo employs a contrastive loss to push dissimilar features apart, enhancing inter-class separation, and a clustering objective to pull together features of the same cluster, promoting intra-class compactness. Our method achieves 91.40% top-1 classification accuracy on CIFAR-10, 68.56% on CIFAR-100, and 78.65% on ImageNet-100 using linear evaluation with a ResNet-18 backbone. By integrating contrastive learning with clustering, CueCo sets a new direction for advancing unsupervised visual representation learning.
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