PVAE: Learning Disentangled Representations with Intrinsic Dimension via Approximated L0 RegularizationDownload PDF

Anonymous

15 Nov 2019 (modified: 05 May 2023)NeurIPS 2019 Workshop DC S2 Blind SubmissionReaders: Everyone
Abstract: Many models based on the Variational Autoencoder are proposed to achieve disentangled latent variables in inference. However, most current work is focusing on designing powerful disentangling regularizers, while the given number of dimensions for the latent representation at initialization could severely influence the disentanglement. Thus, a pruning mechanism is introduced, aiming at automatically seeking for the intrinsic dimension of the data while promoting disentangled representations. The proposed method is validated on MPI3D and MNIST to be advancing state-of-the-art methods in disentanglement, reconstruction, and robustness. The code is provided on the https://github.com/WeyShi/FYP-of-Disentanglement.
Keywords: Disentanglement, Pruning, Intrinsic Dimension, Variational Autoencoder
TL;DR: The Pruning VAE is proposed to search for disentangled variables with intrinsic dimension.
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