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SC-VAE: Sparse Coding-based Variational Autoencoder with Learned ISTA

Learning rich data representations from unlabeled data is a key challenge towards applying deep learning algorithms in downstream tasks. The proposed method learns sparse data representations that consist of a linear combination of a small number of predetermined orthogonal atoms.

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Cite this as

Pan Xiao, Peijie Qiu, Sungmin Ha, Abdalla Bani, Shuang Zhou, Aristeidis Sotiras (2024). Dataset: SC-VAE: Sparse Coding-based Variational Autoencoder with Learned ISTA. https://doi.org/10.57702/mt5x44tf

DOI retrieved: December 2, 2024

Additional Info

Field Value
Created December 2, 2024
Last update December 2, 2024
Defined In https://doi.org/10.48550/arXiv.2303.16666
Author Pan Xiao
More Authors
Peijie Qiu
Sungmin Ha
Abdalla Bani
Shuang Zhou
Aristeidis Sotiras
Homepage https://arxiv.org/abs/2203.15758