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• W2938696877 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 amounts of labeled data." @default.
• W2938696877 created "2019-04-25" @default.
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• W2938696877 date "2019-04-17" @default.
• W2938696877 modified "2023-09-27" @default.
• W2938696877 title "Learning Interpretable Disentangled Representations using Adversarial VAEs" @default.
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