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If you are interested in some maths and have an affinity for concentration inequalities, check out Malach et al.
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Even though the LTH was empirically found out on MLP and CNNs on supervised Studying, I see more and more occurrences of LTH on other coaching paradigms, e.g. this 1 inside the RL context
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Current adversarial Finding out techniques mainly use class labels to crank out adversarial samples that lead to incorrect predictions, which can be then applied to reinforce the coaching on the product for enhanced robustness. Although some current works propose semi-supervised adversarial Understanding approaches that make the most of unlabeled details, they still need class labels. Nonetheless, do we really need class labels at ... [Exhibit comprehensive summary] all, for adversarially strong schooling of deep neural networks? Within this paper, we propose a novel adversarial attack for unlabeled information, which makes the model confuse the occasion-stage identities on the perturbed data samples. Further more, we existing a self-supervised contrastive Mastering framework to adversarially practice a robust neural network without the need of labeled facts, which aims To maximise the similarity among a random augmentation of an information sample and its instance-wise adversarial perturbation.
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