favorite24We present the top classifications for the convolutional and residual network model along with their misclasification on the perturbed data under the FGSM attack on MNIST, as well as, their class activation maps which describe the sensitivity of the classifier on different parts of the input.
favorite0Table 1 describes the results for all five models by evaluating their accuracy on MNIST and CIFAR10, on the actual test data, on the perturbed one after the FGSM perturbation attack and finally on the denoised samples recovered through denoising dictionary learning.
favorite64After the evaluation of dictionary learning as defense mechanism against adversarial perturbations we found that is able to withstand the attacks and provide good results in terms of accuracy for each model on the reconstructed datasets.
favorite5We demonstrate its ability on two different adversarial attacks and record the results in Table 1 and Table 2, which clearly shows that, in overall, each model achieves higher classification accuracy on the perturbed datasets after utilizing dictionary learning to reconstruct the original data from the perturbed samples.
favorite21We examined denoising dictionary learning on MNIST and CIFAR10 perturbed under two different perturbation techniques, fast gradient sign (FGSM) and jacobian saliency maps (JSMA).
favorite10Indeed, unlike precision, MRR and 1-call, the Sudden Death score will change depending on the algorithms we compare against.
favorite12Just as there are several ways of measuring diversity within the objective function of a greedy re-ranking algorithm, there are several ways of measuring the diversity of a list of recommendations, L, for evaluation purposes.
favorite0where p(i |u, a) is the probability of choosing an item i from a set of candidate recommendations RS, produced by a conventional recommender algorithm, given an aspect a and user u.
favorite24A recommender that seeks to diversify may use an objective function that is the same as, or closely related to, the metric used to measure diversity.
favorite4A number of algorithms exist that diversify their top-N recommendation lists; a number of metrics exist that measure diversity.