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This work investigates a novel approach to boost adversarial robustness and generalization by incorporating structural prior into the design of deep learning models and proposes Elastic Dictionary Learning Networks (EDLNets), a novel ResNet architecture that significantly enhances adversarial robustness and generalizat...
Existing research on robust Graph Neural Networks (GNNs) focuses predominantly on undirected graphs, overlooking the trustworthiness inherent in the link directions of directed graphs. In this work, we analyze the limitations of current approaches from both the attack and defense perspectives and redefine the robustnes...
The Threat Conditional Network (TCN) is proposed, grounded in a representation factorization framework that decomposes representation learning into a threat-invariant shared backbone and a lightweight threat-conditional adaptor, enabling flexible and seamless adaptation across an infinite continuum of threat levels dur...
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