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Temporal Gradient Inversion Attacks with Robust Optimization
We investigate a Temporal Gradient Inversion Attack with a Robust Optimization framework, called TGIAs-RO, which recovers private data without any prior knowledge by leveraging multiple temporal gradients.
On Understanding and Mitigating the Dimensional Collapse of Graph Contrastive Learning: a Non-Maximum Removal Approach
Graph Contrastive Learning (GCL) has shown promising performance in graph representation learning (GRL) without the supervision of manual annotations.
Association: Remind Your GAN not to Forget
Neural networks are susceptible to catastrophic forgetting.
A Framework of Randomized Selection Based Certified Defenses Against Data Poisoning Attacks
Random selection based defenses can achieve certified robustness by averaging the classifiers' predictions on the sub-datasets sampled from the training set.
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