1,319 research outputs found
Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial Robustness
Ensemble-based adversarial training is a principled approach to achieve
robustness against adversarial attacks. An important technique of this approach
is to control the transferability of adversarial examples among ensemble
members. We propose in this work a simple yet effective strategy to collaborate
among committee models of an ensemble model. This is achieved via the secure
and insecure sets defined for each model member on a given sample, hence help
us to quantify and regularize the transferability. Consequently, our proposed
framework provides the flexibility to reduce the adversarial transferability as
well as to promote the diversity of ensemble members, which are two crucial
factors for better robustness in our ensemble approach. We conduct extensive
and comprehensive experiments to demonstrate that our proposed method
outperforms the state-of-the-art ensemble baselines, at the same time can
detect a wide range of adversarial examples with a nearly perfect accuracy
Efficient Diverse Ensemble for Discriminative Co-Tracking
Ensemble discriminative tracking utilizes a committee of classifiers, to
label data samples, which are in turn, used for retraining the tracker to
localize the target using the collective knowledge of the committee. Committee
members could vary in their features, memory update schemes, or training data,
however, it is inevitable to have committee members that excessively agree
because of large overlaps in their version space. To remove this redundancy and
have an effective ensemble learning, it is critical for the committee to
include consistent hypotheses that differ from one-another, covering the
version space with minimum overlaps. In this study, we propose an online
ensemble tracker that directly generates a diverse committee by generating an
efficient set of artificial training. The artificial data is sampled from the
empirical distribution of the samples taken from both target and background,
whereas the process is governed by query-by-committee to shrink the overlap
between classifiers. The experimental results demonstrate that the proposed
scheme outperforms conventional ensemble trackers on public benchmarks.Comment: CVPR 2018 Submissio
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