Beyond Cats and Dogs: Semi-supervised Classification of fuzzy labels with overclustering

• URL: http://arxiv.org/abs/2012.01768v1
• Date: Thu, 3 Dec 2020 08:54:25 GMT
• Title: Beyond Cats and Dogs: Semi-supervised Classification of fuzzy labels with overclustering
• Authors: Lars Schmarje and Johannes Br\"unger and Monty Santarossa and Simon-Martin Schr\"oder and Rainer Kiko and Reinhard Koch
• Abstract summary: We propose a novel framework for handling semi-supervised classifications of fuzzy labels. Our framework is based on the idea of overclustering to detect substructures in these fuzzy labels.
• Score: 1.6392706389599345
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A long-standing issue with deep learning is the need for large and consistently labeled datasets. Although the current research in semi-supervised learning can decrease the required amount of annotated data by a factor of 10 or even more, this line of research still uses distinct classes like cats and dogs. However, in the real-world we often encounter problems where different experts have different opinions, thus producing fuzzy labels. We propose a novel framework for handling semi-supervised classifications of such fuzzy labels. Our framework is based on the idea of overclustering to detect substructures in these fuzzy labels. We propose a novel loss to improve the overclustering capability of our framework and show on the common image classification dataset STL-10 that it is faster and has better overclustering performance than previous work. On a real-world plankton dataset, we illustrate the benefit of overclustering for fuzzy labels and show that we beat previous state-of-the-art semisupervised methods. Moreover, we acquire 5 to 10% more consistent predictions of substructures.

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