论文标题
图像群集的学习嵌入：三胞胎损失方法的经验研究
Learning Embeddings for Image Clustering: An Empirical Study of Triplet Loss Approaches
论文作者
论文摘要
在这项工作中，我们评估了两个不同的图像群集目标，即K-均值聚类和相关聚类，在三重态损失诱导特征空间嵌入的背景下。具体来说，我们通过优化三个流行的三胞胎损失版本来训练卷积神经网络来学习判别特征，以便在嘈杂标签的假设下研究其聚类属性。此外，我们提出了一种新的，简单的三重态损失公式，该公式显示了相对于正式聚类目标的理想属性，并且表现优于现有方法。我们评估了K均值的所有三个三重损失公式，并在CIFAR-10图像分类数据集上评估了相关聚类。
In this work, we evaluate two different image clustering objectives, k-means clustering and correlation clustering, in the context of Triplet Loss induced feature space embeddings. Specifically, we train a convolutional neural network to learn discriminative features by optimizing two popular versions of the Triplet Loss in order to study their clustering properties under the assumption of noisy labels. Additionally, we propose a new, simple Triplet Loss formulation, which shows desirable properties with respect to formal clustering objectives and outperforms the existing methods. We evaluate all three Triplet loss formulations for K-means and correlation clustering on the CIFAR-10 image classification dataset.
