30,094 research outputs found
Online Importance Weight Aware Updates
An importance weight quantifies the relative importance of one example over
another, coming up in applications of boosting, asymmetric classification
costs, reductions, and active learning. The standard approach for dealing with
importance weights in gradient descent is via multiplication of the gradient.
We first demonstrate the problems of this approach when importance weights are
large, and argue in favor of more sophisticated ways for dealing with them. We
then develop an approach which enjoys an invariance property: that updating
twice with importance weight is equivalent to updating once with importance
weight . For many important losses this has a closed form update which
satisfies standard regret guarantees when all examples have . We also
briefly discuss two other reasonable approaches for handling large importance
weights. Empirically, these approaches yield substantially superior prediction
with similar computational performance while reducing the sensitivity of the
algorithm to the exact setting of the learning rate. We apply these to online
active learning yielding an extraordinarily fast active learning algorithm that
works even in the presence of adversarial noise
The Effects of JPEG and JPEG2000 Compression on Attacks using Adversarial Examples
Adversarial examples are known to have a negative effect on the performance
of classifiers which have otherwise good performance on undisturbed images.
These examples are generated by adding non-random noise to the testing samples
in order to make classifier misclassify the given data. Adversarial attacks use
these intentionally generated examples and they pose a security risk to the
machine learning based systems. To be immune to such attacks, it is desirable
to have a pre-processing mechanism which removes these effects causing
misclassification while keeping the content of the image. JPEG and JPEG2000 are
well-known image compression techniques which suppress the high-frequency
content taking the human visual system into account. JPEG has been also shown
to be an effective method for reducing adversarial noise. In this paper, we
propose applying JPEG2000 compression as an alternative and systematically
compare the classification performance of adversarial images compressed using
JPEG and JPEG2000 at different target PSNR values and maximum compression
levels. Our experiments show that JPEG2000 is more effective in reducing
adversarial noise as it allows higher compression rates with less distortion
and it does not introduce blocking artifacts
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