Denoising in Representation Space via Data-Dependent Regularization for Better Representation
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DOI: 10.3390/math11102327
Abstract: Despite the success of deep learning models, it remains challenging for the over-parameterized model to learn good representation under small-sample-size settings. In this paper, motivated by previous work on out-of-distribution (OoD) generalization, we study the… read more here.
Keywords: data dependent; better representation; feature; oofs noise ... See more keywords