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Improving Unsupervised Learning with ExemplarCNNs

Publication Type: 
Edited Conference Meeting Proceeding
Most recent unsupervised learning methods explore alternative objectives, often referred to as self-supervised tasks, to train convolutional neural networks without the supervision of human annotated labels. This paper explores the generation of surrogate classes as a self-supervised alternative to learn discriminative features, and proposes a clustering algorithm to overcome one of the main limitations of this kind of approach. Our clustering technique improves the initial implementation and achieves 76.4% accuracy in the STL-10 test set, surpassing the current state-of-the-art for the STL-10 unsupervised benchmark. We also explore several issues with the unlabeled set from STL-10 that should be considered in future research using this dataset.
Conference Name: 
IMVIP 2019
Digital Object Identifer (DOI): 
Publication Date: 
Conference Location: 
Research Group: 
Dublin City University (DCU)
Open access repository: