10,570 research outputs found
Spectral conditions for a graph to be Hamilton-connected
In this paper we establish some spectral conditions for a graph to be
Hamilton-connected in terms of the spectral radius of the adjacency matrix or
the signless Laplacian of the graph or its complement. For the existence of
Hamiltonian paths or cycles in a graph, we also give a sufficient condition by
the signless Laplacian spectral radius
Is Robustness the Cost of Accuracy? -- A Comprehensive Study on the Robustness of 18 Deep Image Classification Models
The prediction accuracy has been the long-lasting and sole standard for
comparing the performance of different image classification models, including
the ImageNet competition. However, recent studies have highlighted the lack of
robustness in well-trained deep neural networks to adversarial examples.
Visually imperceptible perturbations to natural images can easily be crafted
and mislead the image classifiers towards misclassification. To demystify the
trade-offs between robustness and accuracy, in this paper we thoroughly
benchmark 18 ImageNet models using multiple robustness metrics, including the
distortion, success rate and transferability of adversarial examples between
306 pairs of models. Our extensive experimental results reveal several new
insights: (1) linear scaling law - the empirical and
distortion metrics scale linearly with the logarithm of classification error;
(2) model architecture is a more critical factor to robustness than model size,
and the disclosed accuracy-robustness Pareto frontier can be used as an
evaluation criterion for ImageNet model designers; (3) for a similar network
architecture, increasing network depth slightly improves robustness in
distortion; (4) there exist models (in VGG family) that exhibit
high adversarial transferability, while most adversarial examples crafted from
one model can only be transferred within the same family. Experiment code is
publicly available at \url{https://github.com/huanzhang12/Adversarial_Survey}.Comment: Accepted by the European Conference on Computer Vision (ECCV) 201
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