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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...
The findings show that the trust signals encoded in directed graphs can be harnessed to substantially improve the robustness of GNNs and point to a novel and promising direction for strengthening the robustness of graph deep learning.
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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