994 research outputs found
Word-Graph Construction Techniques for Context Analysis
A Nomo-Word Graph Construction Analysis Method (NWGC-AM) is used to graph let the corresponding construction phrases into essential and non-essential citation groups. NMCS-NR, or Nomo Maximum Common Sub-graph edge resemblance, Maximum Common Subgraph Directed Edge resemblance (MCS-DER), and Maximum Common Subgraph Resemblance. The graph resemblance metrics used in this work are called Undirected Edges Resemblance (MCS-UER). The tests included five distinct classifiers: Random Forest, Naive Bayes, K-Nearest Neighbors (KNN), Decision Trees, and Support Vector Machines (SVM).Four sixty one (361) citations made up the annotated dataset used for the studies. The Decision Tree classifier exhibits superior performance, attaining an accuracy rate of 0.98
Boosting Fast Adversarial Training with Learnable Adversarial Initialization
Adversarial training (AT) has been demonstrated to be effective in improving
model robustness by leveraging adversarial examples for training. However, most
AT methods are in face of expensive time and computational cost for calculating
gradients at multiple steps in generating adversarial examples. To boost
training efficiency, fast gradient sign method (FGSM) is adopted in fast AT
methods by calculating gradient only once. Unfortunately, the robustness is far
from satisfactory. One reason may arise from the initialization fashion.
Existing fast AT generally uses a random sample-agnostic initialization, which
facilitates the efficiency yet hinders a further robustness improvement. Up to
now, the initialization in fast AT is still not extensively explored. In this
paper, we boost fast AT with a sample-dependent adversarial initialization,
i.e., an output from a generative network conditioned on a benign image and its
gradient information from the target network. As the generative network and the
target network are optimized jointly in the training phase, the former can
adaptively generate an effective initialization with respect to the latter,
which motivates gradually improved robustness. Experimental evaluations on four
benchmark databases demonstrate the superiority of our proposed method over
state-of-the-art fast AT methods, as well as comparable robustness to advanced
multi-step AT methods. The code is released at
https://github.com//jiaxiaojunQAQ//FGSM-SDI.Comment: Accepted by TI
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