Numerical experiments show that our algorithmic framework has achieved superior and stable performance in various datasets, such as Colored MNIST and Punctuated ...
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Apr 1, 2021 · Abstract:The phenomenon of adversarial examples illustrates one of the most basic vulnerabilities of deep neural networks.
For example, the following command launches a training and model selection task of AIL and IRM algorithms, on the ColoredMNIST and Colored KMNIST datasets.
Numerical experiments show that our algorithmic framework has achieved superior and stable performance in various datasets, such as Colored MNIST and ...
Missing: example | Show results with:example
Sep 12, 2022 · We present a new adversarial training method, Domain Invariant Adversarial Learning (DIAL), which learns a feature representation that is both ...
Adversarial training can be used to achieve robustness against such inputs. Another type of adversarial examples are invariance-based adversarial examples, ...
Adversarial examples are malicious inputs crafted to induce misclassification. Commonly studied sensitivity-based adversarial examples introduce.
Abstract. Learning domain-invariant representation is a dominant ap- proach for domain generalization (DG), where we need to build a classifier.
A Domain Invariant Adversarial Learning Algorithm. Algorithm 1 describes a pseudo-code of our proposed DIALCE variant. As can be seen, a target domain batch ...
To tackle the issue, we propose a resize-invariant method (RIM) and a logical ensemble transformation method (LETM) to enhance the transferability of ...