Rotationally equivariant quantum models can rely on vulnerable invariant statistics such as ring-averaged intensities, leaving them susceptible to classical transfer attacks, but suppressing the associated symmetry sectors substantially improves robustness.
Understanding and mitigating the tradeoff between robustness and accuracy
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CURE is the first multi-norm certified training method that improves union robustness across l_p norms and unseen perturbations on MNIST, CIFAR-10 and TinyImagenet.
CLLAP generates LiDAR-based pseudo-radar data and applies dual-modality contrastive pretraining to boost radar-camera fusion models for 3D detection, showing gains on NuScenes and Lyft datasets.
citing papers explorer
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Feature-level analysis and adversarial transfer in rotationally equivariant quantum machine learning
Rotationally equivariant quantum models can rely on vulnerable invariant statistics such as ring-averaged intensities, leaving them susceptible to classical transfer attacks, but suppressing the associated symmetry sectors substantially improves robustness.
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Towards Generalized Certified Robustness with Multi-Norm Training
CURE is the first multi-norm certified training method that improves union robustness across l_p norms and unseen perturbations on MNIST, CIFAR-10 and TinyImagenet.
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CLLAP: Contrastive Learning-based LiDAR-Augmented Pretraining for Enhanced Radar-Camera Fusion
CLLAP generates LiDAR-based pseudo-radar data and applies dual-modality contrastive pretraining to boost radar-camera fusion models for 3D detection, showing gains on NuScenes and Lyft datasets.