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- W4362655497 abstract "Since the introduction of deep learning, a wide scope of representation properties, such as decorrelation, whitening, disentanglement, rank, isotropy, and mutual information, have been studied to improve the quality of representation. However, manipulating such properties can be challenging in terms of implementational effectiveness and general applicability. To address these limitations, we propose to regularize von Neumann entropy~(VNE) of representation. First, we demonstrate that the mathematical formulation of VNE is superior in effectively manipulating the eigenvalues of the representation autocorrelation matrix. Then, we demonstrate that it is widely applicable in improving state-of-the-art algorithms or popular benchmark algorithms by investigating domain-generalization, meta-learning, self-supervised learning, and generative models. In addition, we formally establish theoretical connections with rank, disentanglement, and isotropy of representation. Finally, we provide discussions on the dimension control of VNE and the relationship with Shannon entropy. Code is available at: https://github.com/jaeill/CVPR23-VNE." @default.
- W4362655497 created "2023-04-07" @default.
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- W4362655497 date "2023-04-03" @default.
- W4362655497 modified "2023-09-27" @default.
- W4362655497 title "VNE: An Effective Method for Improving Deep Representation by Manipulating Eigenvalue Distribution" @default.
- W4362655497 doi "https://doi.org/10.48550/arxiv.2304.01434" @default.
- W4362655497 hasPublicationYear "2023" @default.
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