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3. **Testing on the not-so-easy task.**
We test on the binary classification task described in Section 3.5 of the main paper. The training set contains positive and negative samples of the size 45,000. However, we have to use the feature space described in Section 3.5 of the main paper to compute the feature vectors which are further fed to the binary classifier. The gradient boosting classifier is run on the training data for $10$ boosting iterations with $1000$ trees. We obtain a performance of $84.0\%$ and test on all the $10,000$ samples from the testing set in batches of $1000$ samples. For a positive instance, the test error is $0.33\%$. A close examination of the adversarial samples reveals that they are variants of the legitimate samples with small perturbations such as noise, pixel distortions and color changes.
Additional results
——————
We provide additional results to illustrate the advantage of the gradient boosting classifier and its robustness to dataset irregularities and adversarial attacks. We use the following comparison baselines, when applicable.
– **MacLean et al. [@maclennan2015unsupervised].** A variant of the feature learning technique [@sodani2014unsupervised] that was designed to generate meaningful visual representations from natural images. It is trained with the contrastive loss function of [@ganin2016domain] and trained on the $\textbf{Flickr-Landmarks}$ dataset [@Zhu_2015_CVPR] with $10$ domains that are paired with the landmarks in the image.
– **Random forest [@huho2008localizing].** We run random forest classification on the same datasets for comparison. We randomly select $100$ samples from the training set to create our training data and keep the rest of the samples for testing.
– **Random forest [@
