# Learning Embeddings for Image Clustering: An Empirical Study of Triplet Loss Approaches Source: [Learning Embeddings for Image Clustering: An Empirical Study of Triplet Loss Approaches]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:25:00.051Z Authors: Kalun Ho, Janis Keuper, Franz-Josef Pfreundt, Margret Keuper Published: 2020-07-06T23:38:14.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()