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- W2982288894 abstract "Learning Interpretable representation in medical applications is becoming essential for adopting data-driven models into clinical practice. It has been recently shown that learning a disentangled feature representation is important for a more compact and explainable representation of the data. In this paper, we introduce a novel adversarial variational autoencoder with a total correlation constraint to enforce independence on the latent representation while preserving the reconstruction fidelity. Our proposed method is validated on a publicly available dataset showing that the learned disentangled representation is not only interpretable, but also superior to the state-of-the-art methods. We report a relative improvement of $$81.50%$$ in terms of disentanglement, $$11.60%$$ in clustering, and $$2%$$ in supervised classification with a few amount of labeled data." @default.
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- W2982288894 date "2019-01-01" @default.
- W2982288894 modified "2023-10-14" @default.
- W2982288894 title "Learning Interpretable Disentangled Representations Using Adversarial VAEs" @default.
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- W2982288894 doi "https://doi.org/10.1007/978-3-030-33391-1_5" @default.
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