Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Graph Interpolating Activation Improves Both Natural and Robust
  Accuracies in Data-Efficient Deep Learning   Improving the accuracy and robustness of deep neural nets (DNNs) and adapting
them to small training data are primary tasks in deep learning research. In
this paper, we replace the output activation function of DNNs, typically the
data-agnostic softmax function, with a graph Laplacian-based high dimensional
interpolating function which, in the continuum limit, converges to the solution
of a Laplace-Beltrami equation on a high dimensional manifold. Furthermore, we
propose end-to-end training and testing algorithms for this new architecture.
The proposed DNN with graph interpolating activation integrates the advantages
of both deep learning and manifold learning. Compared to the conventional DNNs
with the softmax function as output activation, the new framework demonstrates
the following major advantages: First, it is better applicable to
data-efficient learning in which we train high capacity DNNs without using a
large number of training data. Second, it remarkably improves both natural
accuracy on the clean images and robust accuracy on the adversarial images
crafted by both white-box and black-box adversarial attacks. Third, it is a
natural choice for semi-supervised learning. For reproducibility, the code is
available at \url{https://github.com/BaoWangMath/DNN-DataDependentActivation}.

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The categories of this paper are: chem-ph
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The categories of this paper are: solv-int
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The categories of this paper are: quant-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: acc-phys
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The categories of this paper are: funct-an
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The categories of this paper are: comp-gas
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The categories of this paper are: nucl-th
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The categories of this paper are: dg-ga
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The categories of this paper are: q-bio
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-lat
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The categories of this paper are: econ
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The categories of this paper are: bayes-an
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The categories of this paper are: alg-geom