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- W4295698814 abstract "In this work we present affinity-VAE: a framework for automatic clustering and classification of objects in multidimensional image data based on their similarity. The method expands on the concept of $beta$-VAEs with an informed similarity-based loss component driven by an affinity matrix. The affinity-VAE is able to create rotationally-invariant, morphologically homogeneous clusters in the latent representation, with improved cluster separation compared with a standard $beta$-VAE. We explore the extent of latent disentanglement and continuity of the latent spaces on both 2D and 3D image data, including simulated biological electron cryo-tomography (cryo-ET) volumes as an example of a scientific application." @default.
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- W4295698814 date "2022-09-09" @default.
- W4295698814 modified "2023-09-26" @default.
- W4295698814 title "Affinity-VAE for disentanglement, clustering and classification of objects in multidimensional image data" @default.
- W4295698814 doi "https://doi.org/10.48550/arxiv.2209.04517" @default.
- W4295698814 hasPublicationYear "2022" @default.
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