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Found 6 papers, 1 papers with code
SynBench: Task-Agnostic Benchmarking of Pretrained Representations using Synthetic Data
Given a pretrained model, the representations of data synthesized from the Gaussian mixture are used to compare with our reference to infer the quality.
Revisiting Contrastive Learning through the Lens of Neighborhood Component Analysis: an Integrated Framework
With the integrated framework, we achieve up to 6\% improvement on the standard accuracy and 17\% improvement on the robust accuracy.
Higher-Order Certification for Randomized Smoothing
We also provide a framework that generalizes the calculation for certification using higher-order information.
Towards Verifying Robustness of Neural Networks Against A Family of Semantic Perturbations
Verifying robustness of neural networks given a specified threat model is a fundamental yet challenging task.
Hidden Cost of Randomized Smoothing
The fragility of modern machine learning models has drawn a considerable amount of attention from both academia and the public.
Towards Verifying Robustness of Neural Networks Against Semantic Perturbations
Verifying robustness of neural networks given a specified threat model is a fundamental yet challenging task.
