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Learning Embeddings for Image Clustering: An Empirical Study of Triplet Loss Approaches

  • 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 noisyIn 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.show moreshow less

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Metadaten
Document Type:Conference Proceeding
Conference Type:Konferenzartikel
Zitierlink: https://opus.hs-offenburg.de/4633
Bibliografische Angaben
Title (English):Learning Embeddings for Image Clustering: An Empirical Study of Triplet Loss Approaches
Conference:International Conference on Pattern Recognition : ICPR (25. : Jan. 10 2021 to Jan. 15 2021 : Milan, Italy)
Author:Kalun Ho, Janis KeuperStaff MemberORCiDGND, Franz-Josef Pfreundt, Margret Keuper
Date of Publication (online):2020/07/06
Page Number:8
Language:English
Inhaltliche Informationen
Institutes:Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019)
Forschung / IMLA - Institute for Machine Learning and Analytics
Institutes:Bibliografie
Formale Angaben
Open Access: Open Access 
Licence (German):License LogoUrheberrechtlich geschützt
Comment:
Preprint; Conference: accepted at ICPR 2021 (Milano)
ArXiv Id:http://arxiv.org/abs/2007.03123