Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Adversarially-Enhanced k-Nearest Neighbors   Recent works have theoretically and empirically shown that deep neural
networks (DNNs) have an inherent vulnerability to small perturbations. Applying
the Deep k-Nearest Neighbors (DkNN) classifier, we observe a dramatically
increasing robustness-accuracy trade-off as the layer goes deeper. In this
work, we propose a Deep Adversarially-Enhanced k-Nearest Neighbors (DAEkNN)
method which achieves higher robustness than DkNN and mitigates the
robustness-accuracy trade-off in deep layers through two key elements. First,
DAEkNN is based on an adversarially trained model. Second, DAEkNN makes
predictions by leveraging a weighted combination of benign and adversarial
training data. Empirically, we find that DAEkNN improves both the robustness
and the robustness-accuracy trade-off on MNIST and CIFAR-10 datasets.

23
The categories of this paper are: solv-int
29
The categories of this paper are: funct-an
15
The categories of this paper are: nucl-ex
28
The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn
8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
12
The categories of this paper are: nucl-th
20
The categories of this paper are: acc-phys
6
The categories of this paper are: astro-ph
13
The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas
21
The categories of this paper are: adap-org
2
The categories of this paper are: math