学习稀疏表示的自动编码器

2022 OITS International Conference on Information Technology (OCIT) Pub Date : 2022-12-01 DOI:10.1109/OCIT56763.2022.00017

Abhinav Sharma, Ruchir Gupta

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引用次数: 0

摘要

许多正则化自编码器学习数据的稀疏表示。这种类型的表示增强了对噪声的鲁棒性和计算效率。在本文中，我们的目标是提供在数据限制较少的情况下，AE鼓励稀疏性的条件。我们展示了输入数据的松弛观测表示，并给出了AE的条件以提高稀疏性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Autoencoders Learning Sparse Representation

Many regularized autoencoders learn a sparse rep-resentation of data. This type of representation enhances robust-ness against noise and computational efficiencies. Our objective in this paper is to provide the conditions under which sparsity is encouraged by AE under a little less restrictive view of data. We have shown a relaxed observed representation of input data and given the conditions on AE to promote sparsity.

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2022 OITS International Conference on Information Technology (OCIT)

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