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    Low-Shot Learning of Plankton Categories (2019)
    Springer
    In:  In: Pattern Recognition - GCPR 2018. , ed. by Brox, T., Bruhn, A. and Fritz, M. Lecture Notes in Computer Science, 11269 . Springer, Cham, Switzerland, pp. 391-404. ISBN 978-3-030-12939-2
    Details
    Publication Date: 2019-09-23
    Description: The size of current plankton image datasets renders manual classification virtually infeasible. The training of models for machine classification is complicated by the fact that a large number of classes consist of only a few examples. We employ the recently introduced weight imprinting technique in order to use the available training data to train accurate classifiers in absence of enough examples for some classes. The model architecture used in this work succeeds in the identification of plankton using machine learning with its unique challenges, i.e. a limited number of training examples and a severely skewed class size distribution. Weight imprinting enables a neural network to recognize small classes immediately without re-training. This permits the mining of examples for novel classes.
    Type: Book chapter , PeerReviewed
    Format: text
    DOI: 10.1007/978-3-030-12939-2_27
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  • 2
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    A Data-Centric Approach for Improving Ambiguous Labels with Combined Semi-supervised Classification and Clustering (2022)
    Springer
    Details
    Publication Date: 2023-01-02
    Description: Consistently high data quality is essential for the development of novel loss functions and architectures in the field of deep learning. The existence of such data and labels is usually presumed, while acquiring high-quality datasets is still a major issue in many cases. Subjective annotations by annotators often lead to ambiguous labels in real-world datasets. We propose a data-centric approach to relabel such ambiguous labels instead of implementing the handling of this issue in a neural network. A hard classification is by definition not enough to capture the real-world ambiguity of the data. Therefore, we propose our method “Data-Centric Classification & Clustering (DC3)” which combines semi-supervised classification and clustering. It automatically estimates the ambiguity of an image and performs a classification or clustering depending on that ambiguity. DC3 is general in nature so that it can be used in addition to many Semi-Supervised Learning (SSL) algorithms. On average, our approach yields a 7.6% better F1-Score for classifications and a 7.9% lower inner distance of clusters across multiple evaluated SSL algorithms and datasets. Most importantly, we give a proof-of-concept that the classifications and clusterings from DC3 are beneficial as proposals for the manual refinement of such ambiguous labels. Overall, a combination of SSL with our method DC3 can lead to better handling of ambiguous labels during the annotation process. (Source code is available at https://github.com/Emprime/dc3).
    Type: Book chapter , NonPeerReviewed
    Format: text
    Location Call Number Limitation Availability
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    OceanRep: Book chapter
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