  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.
