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  4. Learning Embeddings for Image Clustering: An Empirical Study of Triplet Loss Approaches
 
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2021
Conference Paper
Title

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

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.
Author(s)
Ho, Kalun
Fraunhofer-Institut für Techno- und Wirtschaftsmathematik ITWM  
Keuper, Janis
Institute for Machine Learning and Analytics (IMLA), Offenburg University
Pfreundt, Franz-Josef  
Fraunhofer-Institut für Techno- und Wirtschaftsmathematik ITWM  
Keuper, Margret
Data and Web Science Group, University of Mannheim
Mainwork
ICPR 2020, 25th International Conference on Pattern Recognition. Proceedings  
Funder
Deutsche Forschungsgemeinschaft DFG  
Conference
International Conference on Pattern Recognition (ICPR) 2021  
Open Access
DOI
10.1109/ICPR48806.2021.9412602
Language
English
Fraunhofer-Institut für Techno- und Wirtschaftsmathematik ITWM  
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